METHOD AND SYSTEM FOR DETECTING A VALVE STATE

Abstract

Method for detecting a valve state for performing condition monitoring of a valve (3) based on angular velocity data of a valve spindle (4) that opens and closes the valve (3), wherein a sensor unit (1) with at least one semiconductor-type gyroscope sensor (7) is attachable and detachable to the valve spindle (4), and the angular velocity data includes an angular velocity detected by the sensor unit (1) in accordance with a rotary movement of a valve body (30) of the valve (3) from fully open to fully closed or from fully closed to fully open, the valve (3) being a rotary valve that opens and closes or controls a flow path (26a, 27a, 30a) by rotating the valve spindle (4), wherein the angular velocity data indicate a measured value of the angular velocity in one direction.which is essentially the same direction of rotation of the valve spindle (4), the rotary valve is a quarter-turn ball valve or a butterfly valve, and wherein the valve (3) has a structure with a ball of the ball valve and a ball seat (A1, A2) serving as a valve seat or a structure with a valve body of the butterfly valve and a seat serving as a valve seat, wherein the condition monitoring further comprises: , Calculating a rotation angle of the valve spindle (4) using the angular velocity data to calculate opening degrees of the valve body (30), and Calculating transitions of the opening degrees and the angular velocity of the valve body (30) according to a time elapsed from fully open to fully closed or from fully closed to fully closed, and based on a number of openings and closings of the valve body (30), wherein a numerical value representing properties of the changes in angular velocity in at least one time region T1 of an initial opening degree of the valve body (30), a time region T2 of an intermediate opening degree of the valve body (30) and / or a time region T3 of a final opening degree of the valve body (30) is pre-stored as a reference measurement based on the opening degrees of the valve body (30), and wherein the wear condition of the ball seat (A1, A2) or the seat is determined by comparing a numerical value obtained from the angular velocity actually measured by the gyroscope sensor (7) in at least the time region T1 of the initial opening degree of the valve body (30), the time region T2 of the intermediate opening degree of the valve body (30) and / or the time region T3 of the final opening degree of the valve body (30), with the numerical value stored as a reference value corresponding to the number of openings and closings of the valve body (30) during the actual measurement of the angular velocity.

Description

Technical area

[0001] The present invention relates to a method and system for detecting a valve condition, and more particularly to a condition detection system for a rotary valve, such as a ball valve. Technical background

[0002] Generally, in various locations, ranging from large facilities such as various plants and buildings to small facilities such as homes and businesses, various plumbing fixtures are provided, including various pipes and valves, as well as various actuators for automatically controlling these valves. In these plumbing fixtures, rotary valves such as ball valves and butterfly valves are in high demand, for example. In addition, pneumatic actuators are often used as actuators for operating these valves, which are simple in design, easy to downsize, and also excellent in price.

[0003] Normally, in these sanitary facilities, for the automatic control of devices and the like, such as valves and actuators, as well as for the management and maintenance of the operating situation, means for monitoring the condition of these devices and the like by mechanical or artificial means are required. Furthermore, in recent years, the shortage of qualified personnel and the lack of technological knowledge have become increasingly apparent. Not only the condition monitoring for the valves and actuators in sanitary facilities, but also the fault prognosis and lifetime diagnosis of these devices, as well as the ability for more precise condition detection, such asAdequate assessment and discrimination for each fault and / or symptom at product and / or component level, as well as systems capable of managing and controlling the devices based on these detection results from different perspectives, are also increasingly required.

[0004] In particular, a rotary valve such as a ball valve (especially a floating type) or a butterfly valve with a valve seat made of resin such as PTFE or PEEK material that rotates by successively absorbing the complicated and delicate friction action under a driving force by an actuator has been used as a typical opening / closing valve or flow adjustment valve in a wide variety of applications in many environments regardless of the area or location, and its means of accurate condition monitoring and diagnosis have been increasingly demanded in recent years. For example, the ball seat of a ball valve is the core of the valve function and a part that is prone to changing its condition due to material properties, and has the highest necessity for detecting the condition in the operation of the ball valve.

[0005] However, various techniques have been conventionally proposed as a means to monitor at least the state of a valve and / or actuator in a sanitary facility. For example, PTL 1 attempts to check various states of the device based on a characteristic diagram obtained from the operation of a device, in particular the valve and / or actuator.PTL 1 discloses a method for determining the state of a process configuration control component using characteristic diagrams, and more particularly, a method in which a measurement is performed for a characteristic diagram by a device for a predetermined period of time and then a measurement is performed for a characteristic diagram by the same device for another period of time, and these two characteristic diagrams are displayed on a monitor via a computing device, whereby the state of the device is evaluated at the computing device by comparing the characteristic diagrams (whether the state is between limit values).

[0006] On the other hand, in various sanitary facilities as described above, regardless of the structure and situation, artificial means by a worker, i.e., an on-site check of the operating situation of the actuator and / or valve, may be required for various reasons. For example, in a simple facility structure without a sophisticated instrumentation system such as a fieldbus, remote monitoring and control from a control room or the like cannot be carried out, so the worker must go to the site to check individual valves and actuators one by one. Even if a remote monitoring system is in place, if it is out of service or something similar, at least an on-site check is required.

[0007] However, even if there is a pre-determined indicator or something similar on the control shaft of a valve actuator, on-site testing is difficult if the valve or actuator is installed in a complex piping or in a confined space and this sanitary situation is not supported. In addition, a facility configured to be remotely monitored is often configured as one that does not provide for on-site testing in order to simplify the system. In this case, on-site testing is also difficult. In addition, if an attempt is made to reconnect a recording and display device to an existing actuator or valve to promote on-site testing, work to dismantle, attach, or replace equipment such as the actuator, valve, and piping is often required.In addition, if such a device is provided, it may happen that the actuator or the like is enlarged and cannot even be arranged in the pipeline.

[0008] For this reason, the condition of the valve and / or actuator can be easily verified on-site during the on-site inspection work around the sanitary facility described above. Even for a valve and / or actuator that has already been installed or is in operation in the sanitary facility, monitoring devices configured as a device type that can be easily retrofitted are in high demand. Furthermore, in recent years, there has also been a desire for a system configuration that can manage devices such as valves using so-called IOT (Internet of Things) technology and / or cloud computing technology. Furthermore, there is a demand for a system that, while having an existing instrumentation system, can easily detect the condition of a device independently of this existing system.Several proposals for techniques of this kind have already been made, and for example those of PTL 2 and 3 have been proposed.

[0009] PTL 2 describes a predictive maintenance system for valves, in particular the system is configured such that when a magnetic position sensor is housed in a box which can be attached to and detached from a support member on a housing side, magnets each generating a magnetic field to be measured by a sensor are arranged on a spindle side with a predetermined pitch, and a condition such as damage to a ball or seat or failure of an actuator is predicted based on at least one angular position of the spindle detected by an angle detecting mechanism formed therefrom and torque information from a torque sensor included in the spindle, and in particular the condition is evaluated using a torque-angle curve diagram.

[0010] PTL 3 discloses an example configured such that, while a mounting-type valve monitoring unit is mounted via a bracket on the upper part of an actuator mounted on a quarter-turn valve, a sensor capable of reading an actuator state (angular position of a stem) and transmitting an angle change signal to the monitoring unit is mounted on the stem side of the valve, thereby enabling the valve state to be constantly monitored based on the angular position of the stem. For example, a graph of the stem angles versus time is displayed on a graph of this PTL, and a faulty state of the valve is inferred based on its pattern.

[0011] Further systems for determining wear in a valve using a position sensor or an angle sensor are known from PTL 4 to 6. Citation listPatent literature PTL 1: JP 2009 - 543 194 A PTL 2: WO 2016 / 139 376 A1 PTL 3: JP 2015 - 528 085 A PTL 4: DE 10 2008 064 359 A1 PTL 5: WO 2015 / 001 548 A1 PTL 6: US 2015 / 0 088 434 A1 Summary of the inventionTechnical problem

[0012] Although the technique in PTL 1 can be considered broadly applicable to general targets in terms of comparing and evaluating the characteristic diagrams of the device, specific means such as a method for acquiring characteristic diagrams are not disclosed. For example, it is impossible to perform precise condition detection and diagnosis of individual specific targets for each type of valve, such as a ball valve or butterfly valve, or for each component, such as a valve seat or packing, and moreover, with regard to a damage condition, replacement timing, etc. Therefore, it cannot be said that the technique is capable of performing the above-described precise condition detection and diagnosis for each specific target, such as a ball valve or butterfly valve.

[0013] In this regard, although a diagram of the actuator pressure and the movement position of a pneumatic actuator is specifically described as an example of the characteristic diagrams, obtaining this characteristic diagram from an existing actuator—that is, after connecting it to the piping—requires removing a piping system that draws in and discharges air pressure, installing a pressure sensor or similar device in the actuator, and then reassembling the actuator. Simply retrofitting it as a monitoring device on a device or similar device is therefore not possible.

[0014] Even in the device configuration of PTL 2 and 3, the separate attachment of a measurement target element, such as a spindle, is required at least on one side of the valve or actuator. Therefore, the devices of PTL 2 and 3 are of the external information measurement type. Since this measurement element is required, the number of device components and the manufacturing and management steps increase. The time and labor required for attachment impairs manageability. Moreover, the application target is limited and usability is impaired. These points can be considered disadvantages. Therefore, in view of the problems described above, the techniques in PTL 2 and 3 are still insufficient to achieve simple construction and easy retrofitting.

[0015] In addition, in PTL 2 and 3, the state of the device is detected solely based on the data of the angle sensor that detects the angle of the rotating shaft, such as a spindle. However, as will be described later, the sensor consisting of at least the angle sensor is still insufficient to detect in detail the movement, especially of the rotating shaft that rotates as it receives the random action of friction, with a simple structure to achieve detailed analysis of the movement, and in particular, it is insufficient as a data acquisition means for use in life diagnosis. Specifically, in the angle sensor, only a linear or smoothly curved curve can be detected as temporal transitions of the angle. This means that only rough, insufficient movement data with low accuracy is detected in the angle sensor.Therefore, it is impossible to solve the problems described above to achieve condition detection and diagnosis of a target more accurately using angle information from the angle sensor.

[0016] In fact, in the angle-time diagram shown in PTL 3, each curve of the real-time measured values ​​takes on a linear or smooth curve, so it can be said that only a rough rotational movement characteristic is captured. In particular, although measurement curves oscillate like waves are shown, these are merely examples where the valve rotation is reversed, and only a simple overshoot and extremely rare movement is captured.

[0017] Furthermore, for the problems described above, at least for the condition monitoring of the valve and / or the actuator, it is of course necessary to provide a sensor that can measure these conditions (such as a rotation angle). In particular, a sensor that is easily retrofittable is considered effective. Several techniques of this type, e.g. using an inertial sensor (Inertial Measurement Unit (IMU)) attached to a valve and / or an actuator, have been conventionally proposed, but these are merely proposed as valve opening meters that measure a degree of opening (rotation angle) of a valve. Therefore, even if a sensor, such asTherefore, if an inertial sensor, which can be easily retrofitted to a target product, is provided to the valve and / or actuator, it may not be known what data should be acquired by this sensor and how, and how the acquired data will be used to solve the problems described above (precise condition detection and diagnosis), etc., making it impossible to solve the problems described above.

[0018] The present invention has therefore been developed to solve the problems described above and aims to provide a system for detecting a valve condition that can be easily retrofitted into various existing or in-service valves (rotary valves) and actuators, in particular also in installations that are not supplied with mains power, and enables detailed and accurate detection of the condition as well as diagnosis or fault prognosis for the valve or actuator. Solution to the problem

[0019] To solve the above-mentioned problems, a method having the features of claim 1 and a system having the features of claim 2 are provided. Advantageous further developments can be found in the description and the accompanying drawings.

[0020] A monitoring unit, also referred to as a sensor unit, comprising at least one semiconductor-type gyroscopic sensor is attachably and detachably secured to the valve spindle, and the angular velocity data is detected by this monitoring unit in accordance with a rotational movement of the valve body from the fully open or fully closed state to the fully closed or fully open state.

[0021] The valve is a rotary valve that opens and closes or controls a flow path by rotating the valve stem, and the valve stem is preferably a rotary shaft formed from an output shaft and a control shaft of an automatic valve via the actuator or from a stem of a manual valve via a handle.

[0022] More specifically, the rotary valve is a quarter-turn ball valve or a butterfly valve.

[0023] Condition monitoring includes detecting the wear condition of a valve seat, in particular a stuffing box packing and / or a spindle bearing.

[0024] The angular velocity data is used to further calculate the angle of rotation of the valve spindle.

[0025] Generally speaking, the system comprises a valve, a sensor unit attached to this valve and a server communicatively connected to this sensor unit having a database, wherein the system is in particular configured to detect a wear condition of a wear part on the basis of a characteristic value contained in measurement data measured by a sensor included in the sensor unit from a valve spindle that opens and closes the valve.

[0026] The sensor is a gyroscopic sensor that measures the angular velocity at which the valve spindle rotates.

[0027] According to a first advantageous development, the invention is directed to the system for detecting a valve state, in which the feature value consists of feature data formed wholly or partly from: a time from full opening of the valve to a predetermined opening degree or full closing, which appears in an angular velocity diagram obtained from the angular velocity data in the axial center direction of the valve spindle; a time from a predetermined opening degree to full closing; the number of steep gradients and the position, magnitude, or width of each steep gradient of the angular velocity included in a predetermined time range; and / or a time until the angular velocity reaches a maximum value or a local maximum value and a magnitude or width of the maximum value or the local maximum value.

[0028] According to a second advantageous development, the invention is directed to the system for detecting a valve state, wherein a reference data table is stored in the database, which is formed from a plurality of label data and the feature data in accordance with a predetermined opening / closing count of the valve for each specific state, the sensor unit and / or the server is / are provided with a first anomaly diagnosis means configured to detect the wear state and perform an anomaly diagnosis of the valve, and this first anomaly diagnosis means comprises a data generation means for specific data, which generates specific data formed from a specific state of the valve, an opening / closing count of the valve, and specific feature data based on angular velocity data, a data acquisition means,which acquires from the reference data table reference data having an opening / closing count value equal to the opening / closing count value of the specific data and a substantially equal specific feature value, and a comparison and determination means which compares all label data included in this acquired reference data with a predetermined threshold value to acquire a predetermined determination result.

[0029] According to a third advantageous development, the invention is directed to the system for detecting a valve condition, wherein a learning model is stored in the database that calculates label data derived from the feature data, the sensor unit and / or the server is / are provided with a second anomaly diagnosis means configured to detect the wear condition and perform an anomaly diagnosis of the valve, and this second anomaly diagnosis means comprises a feature value generation means that generates the feature data based on the measurement data, a derived label data calculation means that calculates a piece of derived label data via the learning model based on the feature data, and a comparison and determination means that compares this derived label data and a predetermined threshold value to obtain a determination result.

[0030] According to a fourth advantageous development, the invention is directed to the system for detecting a valve condition, wherein a learning model is stored in the database, which calculates model data from accumulated feature data, the sensor unit and / or the server is / are provided with a third anomaly diagnosis means configured to detect the wear condition and perform an anomaly diagnosis of the valve, and this third anomaly diagnosis means comprises a feature value generation means that generates predetermined feature data based on the measurement data, a data accumulation means that accumulates the feature data in the database and generates the accumulated feature data, a data control means that performs a predetermined control, a model data calculation means that calculates the model data via the learning model based on the accumulated feature data, an index calculation means,which calculates a predetermined index from the model data and new feature data, and a comparison and determination means which compares the index and a predetermined threshold value to obtain a determination result.,

[0031] According to a fifth advantageous development, the invention is directed to the system for detecting a valve condition, wherein the wear component is a valve seat, the valve is a rotary valve, the sensor unit is a single unit capable of wireless communication with the server and includes a power supply, and this sensor unit is attachably and detachably mounted in a mode in which it can rotate with the valve spindle.

[0032] According to a sixth advantageous development, the invention is directed to the system for detecting a valve state, wherein the label data are formed from dimension data consisting of a dimension of the wearing part in a wear-free state and / or from leakage quantity data formed from a leakage quantity when the valve is completely closed.

[0033] According to a seventh advantageous development, the invention is directed to a system for detecting a valve condition, comprising a valve, a gyroscopic sensor unit attached to said valve, and a server communicatively connected to said gyroscopic sensor unit and comprising a database, wherein said database stores a second reference data table comprising output data and product data in accordance with an opening / closing count of the valve, the sensor unit and / or the server is / are provided with a fourth anomaly diagnostic means configured to detect a wear condition of a wear part contained in the valve and to perform an anomaly diagnosis of the valve, said fourth anomaly diagnostic means comprising a data generation means that generates measurement data including output data and product data,which are measured by the gyroscopic sensor unit in accordance with an opening / closing count value of the valve, a data acquisition means that acquires second reference data from the second reference data table having output data substantially equal to the output data of the valve contained in these measurement data, and a failure determination means that determines a failure prediction of the valve based on usage frequency data of the valve contained in these acquired second reference data. Advantageous effects of the invention

[0034] Advantageously, the valve stem of the valve is a part connected to the valve body and directly captures its movement. It is therefore suitable as a part for observing the movement of the valve body, directly reflecting the behavior and symptoms of the valve at the current moment, such as the condition of the valve seat due to friction. Since the valve stem is directly related to various important parts such as bearings and packing, the conditions of these parts are also directly reflected.

[0035] On the other hand, unlike position (angular) data, (angular) velocity data with at least high accuracy essentially indicates information that well reflects the motion characteristics of a target at the time of measurement. For example, in the case of random motion under the action of friction, subtle motion characteristics not reflected in the position data are also reflected. Therefore, by taking the angular velocity data of the valve spindle of the valve as a basis, it is possible to achieve simple and precise condition monitoring, diagnosis, and service life prediction of the valve.

[0036] Advantageously, the gyroscopic sensor allows the rotational motion (rolling friction) to be recorded as an angular velocity diagram with a nonlinear region containing a multitude of peak values. This makes it easy to obtain detailed, elusive diagnostic information, allowing the valve condition to be assessed in detail based on this data. Because the gyroscopic sensor detects rotational motion with respect to the reference axis with high accuracy, it is also very useful as a sensor for life prediction, even if it is a low-cost, low-performance, or general-purpose sensor.

[0037] Furthermore, at the stage of actual use, where essentially only the monitoring unit needs to be detachably attached to the valve stem part of each target product, a simple system for detecting a valve status can be configured, independent of the existing system. The breadth of the attachment target (such as product type, installation situation, and whether an operation is currently in progress) and the attachment method are also very broad. Therefore, the system can be easily retrofitted to any of the various target products by any employee. Furthermore, the functions can be concentrated in a compact form as a monitoring unit, the manageability or usability as a product is excellent, also in terms of cost, etc.

[0038] Since at least the rotating shaft of the rotary valve is taken as the measurement object, the target movement to be measured by the gyroscopic sensor consists only of a simple axial rotational movement with respect to the non-displaced reference axis, so that the gyroscopic sensor's function as a sensor for axial rotational movement can be best performed. Therefore, precise movement measurement can be performed with a simple setup.

[0039] According to the invention, it is possible to detect the state of a quarter-turn ball valve or butterfly valve, which are widely used in various scenarios, regardless of whether they are manual or automatic valves, and which are in high demand for various needs now or in the future. Even when calculating the angle from angular velocity data obtained from, among other things, the movement of the valve, the accumulation range (angular deflection) is small, a maximum of 90 degrees. Thus, accumulated errors can only be within a small range, which can also lead to savings in computing resources and device structure.

[0040] The valve seat, gland packing, and / or stem bearing each occupy an important part of the valve, and their behavior, including the wear condition, affects the valve's important functions. On the other hand, these are wear parts installed internally, so the wear condition of these parts is usually determined by disassembling / removing the valve assembly, removing components, and visually inspecting them. It is difficult to detect the wear condition, at least quickly, in a simple and non-destructive manner. However, according to the present invention, detailed diagnosis can be achieved with utmost ease even for these important internal components and parts related to the product's service life.

[0041] The information about the angle and opening degree is important as basic information about the valve in different scenes, and the angular velocity data can be effectively used at least for the angle calculation.

[0042] Preferably, the wear condition of the valve's wear parts is diagnosed based on the characteristic value of the measurement data obtained from the actual operation of the valve. Thus, anomaly diagnosis is performed using a so-called non-destructive inspection scheme for detecting the condition of the device from a control signal. This is nothing more than allowing rational replacement with regard to the maintenance of the entire system in a piping system where multiple valves are arranged on a single pipeline. This means that even if maintenance is only performed on a single valve, the operation of its pipeline must be stopped, and the entire replacement is carried out under the given circumstances, even if there is another arranged valve that is still usable.Since a valve with a lower usage frequency has a longer practical life expectancy than other valves of the same type and therefore does not need to be replaced, the present invention can reduce maintenance costs. Since data is retained for the entire period from the time the product is new to the time the product fails, the usage status can be detected even if the gyro sensor is attached to a valve whose service life has expired to a certain extent. In this way, a failure prediction control system can be quickly developed on the market.

[0043] The rotational movement of the valve spindle, in particular, has a very strong tendency to be characterized by an angular velocity diagram, which is captured by measurement with the gyro sensor, and thus the processing of the measurement data is also very simple. The invention is very suitable for detecting the state of the measurement object, even with regard to a large amount of data processing, such as machine learning in particular.

[0044] According to the first embodiment of the invention, the feature value is limited to having only one of several recognizable diagram patterns, so that it is possible to extract feature data that is easy to process.

[0045] According to the second embodiment of the invention, based on the unique pattern obtained from the angular velocity data, the valve can be easily diagnosed from the reference table through simple processing. Reference data obtained from the actual operation of the valve product can also be used very effectively. Furthermore, predefined machine learning can also be applied.

[0046] According to the third embodiment of the invention, anomaly diagnosis of the valve is performed using a machine learning method with unique, target-specific data based on the label data obtained through this machine learning. With the development of machine learning technology in recent years, improvements in computer performance and data storage capacity, and reduction in costs, it is thus possible to easily perform anomaly diagnosis with target-specific accuracy and high reliability.

[0047] According to the fourth embodiment of the invention, it is possible to perform a diagnosis based on real-time data in accordance with the individuality of the product in actual operation, which is used under specific conditions. Thus, the accuracy and reliability of the diagnosis can be improved in accordance with the product, and at least during system configuration, it is only necessary to create a database for a specific product individual in actual operation.

[0048] Since the sensor unit according to the fifth embodiment of the invention is a single unit that can communicate wirelessly, retrofitting and removal at the device in which the valve is arranged or the valve status is monitored is very simple and the unit itself is also easy to handle.

[0049] According to the sixth development of the invention, a value important for the valve characteristic is selected as a label, and thus the invention is very suitable for the anomaly diagnosis of the valve.

[0050] According to the seventh aspect of the invention, when all the data from a state where the valve is new to a state where the valve fails is stored in advance in the second reference data table, by the failure determination means that determines the prediction of valve failure based on the usage frequency data of the valve, notification of a replacement time can be made gradually every month, for example, three months in advance. Since the data for the entire period from a time when the product is new to a time when the product fails is retained, the usage state can be detected even if the gyro sensor is attached to a valve whose usage period has expired to a certain extent. In this way, a failure prediction controller can be quickly developed on the market. Brief description of the drawings Fig.1 is an external perspective view of a ball valve equipped with an actuator of the present example. Fig. 2 is an external plan view of Fig. 1. Fig. 3 is a sectional view of a section along a line AA in Fig. 2. Fig. 4 is a block diagram showing the internal structure of a monitoring unit of the present example. Fig. Figure 5 is an example of an angular velocity diagram recorded in one embodiment (Test No. 10) under certain conditions. Fig. 6 is an example of the angular velocity diagram taken in the embodiment (Test No. 10) under certain conditions. Fig. 7 is an example of the angular velocity diagram taken in the embodiment (Test No. 10) under certain conditions. Fig. Figure 8 is an example of the angular velocity diagram taken in the embodiment (Test No. 10) under certain conditions. Fig. 9 is an example of the angular velocity diagram taken in the embodiment (Test No. 10) under certain conditions. Fig. 10 is a sectional view along a line BB of Fig. 3, the sectional view showing an example of a ball valve in the fully closed state. Fig. 11 is a sectional view along the line BB of Fig. 3, where the sectional view shows an example of the ball valve with an opening degree in the middle. Fig. 12 is a sectional view along the line BB of Fig. 3, where the sectional view shows an example of the ball valve with an opening degree in the middle. Fig.13 is a sectional view along the line BB of Fig. 3, where the sectional view shows an example of the ball valve with an opening degree in the middle. Fig. 14 is a sectional view along the line BB of Fig. 3, where the sectional view shows an example of the ball valve in the fully open state. Fig. Figure 15 is an example of an angular velocity diagram taken in one embodiment (Test No. 2) under certain conditions. Fig. 16 is an example of the angular velocity diagram taken in the embodiment (Test No. 2) under certain conditions. Fig. 17 is an example of the angular velocity diagram taken in the embodiment (Test No. 2) under certain conditions. Fig.Figure 18 is an example of the angular velocity diagram recorded in one embodiment (Test No. 8) under certain conditions. Fig. 19 is an example of the angular velocity diagram taken in the embodiment (Test No. 8) under certain conditions. Fig. Fig. 20 is an example of the angular velocity diagram taken in the embodiment (Test No. 8) under certain conditions. Fig. Figure 21 is an example of the angular velocity diagram recorded in one embodiment (Test No. 11) under certain conditions. Fig. Fig. 22 is an example of the angular velocity diagram taken in the embodiment (Test No. 11) under certain conditions. Fig.23 is an example of the angular velocity diagram taken in the embodiment (Test No. 11) under certain conditions. Fig. 24 is a schematic descriptive diagram describing an example of a situation in which a wear amount of a ball seat is measured. Fig. Figure 25 is an example of an X-axis angular velocity plot taken in another example (initial motion). Fig. Figure 26 is an example of a Y-axis angular velocity plot taken in another example (initial motion). Fig. Figure 27 is an example of a Z-axis angular velocity plot taken in another example (initial motion). Fig. Figure 28 is an example of an X-axis angular velocity graph taken in another example (twenty thousand times). Fig. Figure 29 is an example of a Y-axis angular velocity graph taken in another example (twenty thousand times). Fig. Figure 30 is an example of a Z-axis angular velocity plot taken in another example (twenty thousand times). Fig. 31 is a block diagram illustrating a general outline of a valve condition sensing system of the present invention. Fig. 32 is a flowchart illustrating a general outline of an abnormality diagnosis process of the valve condition detecting system of the present invention. Fig. 33 is a data flow diagram illustrating an anomaly diagnosis process (normal flow) by fourth anomaly diagnosis means. Fig. 34 is a data flow diagram illustrating an anomaly diagnosis process (reference generation flow) by the fourth anomaly diagnosis means. Fig. Figure 35 is an example of an acceleration diagram recorded in another example (initial movement). Fig. Figure 36 is an example of an accelerogram taken in another example (twenty thousand times). Description of the embodiments

[0051] Hereinafter, the system for detecting a valve state in an embodiment of the present invention will be described in detail with reference to the drawings. Fig. 1 is an external perspective view of an actuator-equipped ball valve in a state in which a monitoring unit 1 (also referred to as a sensor unit 1) is attached to an actuator 2 (also referred to as a rotary actuator 2) in the present embodiment, and Fig. 2 is an external plan view of the actuator 2 in Fig. 1 from the top. Fig.1 also shows a fully opened state of a valve 3, wherein the X-axis coincides with an axial center direction of the flow path, the Y-axis extends in a direction (upward in the drawing) in which a control shaft 4, also referred to as a valve spindle 4, extends outward with respect to this X-axis, and the Z-axis is a right-turned direction on the X and Y axes.

[0052] In Fig. 1 and Fig.2, for example, for a casing (housing means) of a monitoring unit 1, any external shape, material, and so on can be selected as long as the casing has a compact size and weight so that it can be easily carried with one hand. In the present example, it is a resin casing molded in the shape of a rectangular plate with a length of approximately 15 cm × 10 cm and a thickness of approximately 3 cm, and as a finished product, it weighs on the order of several hundred grams. For example, product information, model number, or attachment direction (method of use), etc., are indicated on a front side. On a back side, a predetermined fastening part (not shown) consisting of a female screw hole, an adhesive surface, etc., to which a fitting piece 5 can be attached is provided. Alternatively, the casing may be formed, for example, in the shape of a circular disk of approximately the same size.

[0053] The adapter 5 is an example of a fastening device and, in this example, consists of an L-shaped metal plate, with one side surface serving as a mounting surface that is firmly attached to the rear of the monitoring unit 1, and the other side surface being firmly attached with a bolt 6 to an upper end portion of the control shaft 4 of the actuator 2. The NAMUR standard is a standardized interface standard (VDI / VDE 3845-2010) for actuators, and it defines the dimensions for valve attachment and the attachment of an accessory to an upper portion of the actuator. If the actuator 2 complies with this NAMUR standard, a screw nut part (not shown) that complies with this standard is attached to an upper end portion of the control shaft 4. By using this nut part, the monitoring unit 1 can be easily retrofitted to the actuator 2 via the adapter 5.

[0054] Here, in an actuator already in use, an accessory device such as an opening / closing limit switch may be mounted on an upper part of the control shaft 4. In this case, by using the L-shaped metal plate of the present example, the monitoring unit 1 can be fixed to the control shaft 4 while ensuring an upper space of the control shaft 4 with the accessory device attached thereto.

[0055] In Fig. 1, Fig. 2 and Fig. 4 is a gyro sensor 7 (shortly sensor 7), also referred to as a semiconductor gyro sensor 7, which is a rectangular semiconductor element incorporated in the monitoring unit 1 of the present example, applied to an inner substrate so that it is parallel to the short sides and the long sides of the rectangular monitoring unit 1. In particular, in Fig. 1 and Fig.2, the monitoring unit 1 is mounted so that it is aligned parallel to the XY plane. In this state, the yaw axis of the gyro sensor 7 coincides with the direction of the Z axis, and the roll and pitch axes coincide with the directions of the Y axis and the X axis, respectively.

[0056] In Fig.2, in the present example, at a reference position where the valve 3 is in the fully open state, the gyroscopic sensor 7 built into the monitoring unit 1 is provided so as to be positioned doubly eccentrically to the position of the control shaft 4. Specifically, the monitoring unit 1 is arranged via the fitting 5 by an eccentric distance α (to the right in the drawing) parallel to the axial center position of the control shaft 4 (axial center direction of the flow paths 26a and 27a) and, corresponding to the position of the gyroscopic sensor 7 on the substrate, by an eccentric distance β (downward in the drawing) from the axial center position of the bolt 6 (vertical direction to the axial center of the flow paths 26a and 27a). In the present example, α=18 mm and β=33 mm are set.

[0057] By arranging the gyro sensor 7 at this double eccentric position, at least when the monitoring unit 1 is attached to a target product, a free space where no other element is located can be easily utilized, and the monitoring unit 1 can be easily and compactly attached to the target product and can also be easily retrofitted on-site for products of different sizes, structures, and orientations. In particular, the processability of the rough fastening is favorable, and the scope of fastening targets is also broadened. In addition, although the position of the monitoring unit 1 is kept close to the position of the control shaft 4, a large rotation radius (α 2+ β 2 ) 1 / 2from the control shaft 4, which is a rotating shaft as the measurement target. It should be noted that the arrangement of the gyro sensor on the structure is not limited to the fitting 5 and can be fixed in the axial direction at a center position of the control shaft by a fitting that is attached in the form of a clamp on the control shaft.

[0058] In this way, the monitoring unit 1 with at least the semiconductor gyro sensor 7 is attachably and detachably mounted on the valve spindle. As described below, in the present invention, condition monitoring, diagnosis, and service life prediction of the valve are also performed based on angular velocity data of the valve spindle that opens and closes the valve 3. This angular velocity data includes data ( Fig. 5 to Fig. 9, Fig. 15 to Fig.23), which are formed in an angular velocity diagram corresponding to a rotational movement of the valve body (ball 30) from the fully open or fully closed state to the fully closed or fully open state, as detected by the monitoring unit 1. Furthermore, although the monitoring unit 1 is attached to the control shaft 4 in the present example, it can be attached to an output shaft 14 using suitable fastening means.

[0059] In Fig.Figure 4 shows an example of a basic structure integrated into the monitoring unit 1, shown as a block diagram. This structure is not limiting, and any structure can be selected depending on the implementation. However, the unit includes at least the gyro sensor 7 (angular velocity sensor) as a motion sensor. The gyro sensor 7 of the present example is a vibration gyro sensor using MEMS (Micro Electric Mechanical System) technology, an IC type semiconductor device located in the internal substrate.

[0060] The gyro sensor is a three-axis gyro sensor capable of measuring rotation in three orthogonal XYZ-axis directions, and such a sensor is currently used in various consumer products. Specifically, the L3GD20 manufactured by STMicroelectronics is used. Its characteristics include: supply voltage: DC 3.3 V (operating range: DC 2.4 V to DC 3.6 V); current consumption: 6.1 mA; measurement range: ±250 dps (resolution: 0.00875 dps), ±500 dps (resolution: 0.0175 dps), and ±2000 dps (resolution: 0.07 dps). However, these characteristics are not limiting, and it goes without saying that any selection and adjustment can be made according to the implementation.

[0061] In addition, the monitoring unit 1 contains Fig.4 includes at least a CPU 8 (central processing unit), a memory 9, a communication module 10, a power supply 11, and an IC tag 12, as well as a temperature sensor in the present embodiment described below. Furthermore, in addition to the gyro sensor 7 described above, an acceleration sensor and a magnetic sensor (not shown) may be combined for use in the system of the present invention. For energy saving, a piezo sensor may also be combined to activate the gyro sensor when needed.

[0062] The CPU 8 is intended to include a cache. A cache with general specifications can be used, and each can be selected according to the implementation. In particular, a processing capacity capable of achieving each function described below (especially the power-saving function) is required. This CPU 8 is connected to peripheral elements such as the memory 9 and the communication module 10 via a bus. As with the CPU 8, the memory 9 is also selected according to the implementation, with capabilities (capacity and speed) capable of achieving each function described below. If sequential power supply is not assumed, non-volatile memory is preferable. In addition, a capacity capable of sufficiently reading various applications that execute the power-saving function, etc., is suitable.

[0063] The communication module 10 is preferably a wireless near-field communication module. In this example, Bluetooth (registered trademark) is used. Via this communication module 10, at least the angular velocity data and its transition through the gyro sensor 7 are communicated with an external, not-shown portable terminal 44. This portable terminal 44 enables the status recording and display testing of an automatic valve via a dedicated application. In addition to Bluetooth (registered trademark), infrared rays, Wi-Fi Direct, or similar technologies can also be used.

[0064] The power supply 11 includes a predetermined power supply conversion circuit, and each can be selected according to the implementation. The power supply is, for example, an independent power supply by a button battery or a battery power supply. In the case of a button battery, for example, in the attachment and removal position, a disk-shaped battery cover is engaged and fixed with a hole part formed in a cover body via a sealing member (not shown), and is provided attachable and detachable at a predetermined angle by a negative driver or the like. The power supply 11 connects each of the elements including the gyro sensor 7, the CPU 8, the memory 9, and the communication module 10, and serves as a drive source for them.

[0065] The IC tag 12 collects unique information about the actuator 2 and / or the valve 3. This information includes at least (1) the model type and order number of the actuator 2 and / or the valve 3 and (2) a URL for downloading application software. This accumulated information is input into a special terminal or similar device, not shown. The URL for downloading application software is intended for portable devices. Application software can be obtained from this download URL.

[0066] The monitoring unit 1 described above has, as part of a condition monitoring and detection function for the target product (valve 3 and / or actuator 2), at least a data measurement function and a function for accumulating the measured data. The data of a measurement target includes angular velocity data in the control shaft 4 for at least each time or each opening / closing count. The detected data is output from the gyro sensor 7 and accumulated in the memory 9 via data processing in the CPU 8. In this case, the data can be converted into data that can be displayed as a diagram on an external monitor. This data can also be set to be accumulated in the memory 9 after at least simple data processing has been performed, such asThe so-called "interpolation," in which this data is accumulated by the CPU 8 at constant intervals with a data average or with a predetermined filtering (noise reduction) in the memory 9. In response to a request from the portable terminal 44, the accumulated data is transmitted to the portable terminal 44 via the wireless near-field communication module 10, which is Bluetooth (registered trademark). From this portable terminal 44, the records of the status of the actuator 2 and / or the valve 3 are displayed and reviewed.

[0067] Furthermore, as described below, based on the monitored and detected state of the valve, the monitoring unit 1 may include various functions required in a process (flow formed by various process steps) to perform symptom diagnosis, such as failure prediction at the component / part level of the valve (target product), optional functions such as energy saving function and data proofreading function by an auxiliary sensor (e.g., an acceleration sensor) or a function to be performed by a predetermined externally acquired application.

[0068] These various functions can also be performed in the monitoring unit 1 or in an external server, etc., and are allocated accordingly as needed. Particularly, when the structure is such that the angle data can be further calculated based on the angular velocity data, it is suitable, in view of data accuracy, power consumption, and load, to use an acceleration sensor to correct the drift of the gyro sensor 7 after a summation calculation (e.g., the rectangular method) through the four basic operations without the intervention of integration means. Furthermore, a predetermined database for use in data analysis from the monitoring unit 1 can be established in an external server, etc.

[0069] In the present invention, an angular velocity diagram is essentially created from the measured angular velocity data, and various diagnostic processes, including a service life prediction process, are performed based on the shape / pattern analysis of this diagram data. These diagnostic processes include, for example, a process of recognizing and evaluating the diagram pattern, a process of retrieving the existing accumulated data (diagram data for comparison) for comparison with the acquired diagram pattern, a symptom determination process, and a process of outputting and displaying the result, an alarm, etc. A physical or logical system is configured so that these various processes can be appropriately executed.

[0070] In addition, a function for measuring and storing various unique data, a function for externally outputting and displaying this data, or a function for use in any of the processes described above may be provided, wherein the various unique data includes, as unique information about the target product: fluid pressure, viscosity, and temperature; temperature and humidity of a product environment; open / close count and operation time of the valve after installation; supply pressure and activation speed of the actuator; or, in the case of a ball valve, material and wear coefficient of the ball seat or stuffing box packing, as well as the size of the ball and the flow path.

[0071] In particular, the gyro sensor 7 has high power consumption, and the monitoring unit 1 of the present invention is used to maintain it at a level of several years at most for a long period of time. Therefore, from the viewpoint of energy saving, it is important to select a combination of the gyro sensor 7 and the power supply 11, and the power saving function is also important. For example, the CPU 8 may normally be in a power-saving state in which it receives data from the gyro sensor 7 but does not accumulate this data in the memory 9. When the operation of the actuator 2 is detected, the power-saving state may be canceled, and at least the angular velocity data detected by the gyro sensor 7 may be accumulated in the memory 9.The state can return to the power-saving state after the state in which the operation of the actuator 2 is not detected continues for a predetermined period. Note that, for example, a self-generating gyro sensor (e.g., vibration power generation or photovoltaic power generation) can be used as the power-saving function.

[0072] On the other hand, Fig. 1 to Fig. 3 In the present example, the monitoring target products for the monitoring unit 1 are described as the pneumatic rotary actuator 2 in a double-actuated Scotch-Yoke crank drive structure and the 90-degree ball rotary valve 3.

[0073] In Fig. 1 to Fig.3, a conversion mechanism 13 is provided within the main body of the actuator 2, which converts reciprocating motion into rotary motion. The rotational force of this conversion mechanism 13 can be transmitted from the output shaft 14 to a spindle 15 of the ball valve 3. The conversion mechanism 13 consists of a structure in which a Scotch-yoke crank mechanism 35 for transmission to the rotary shaft (valve spindle) and paired pin rollers 16 with which this Scotch-yoke crank mechanism 35 engages are provided on a piston rod 17, and these are installed in a housing 18.

[0074] On one side of the housing 18, which is Fig.3 is a right side, a cylinder part 19 is attached. A cylinder housing 20 of this cylinder part 19 houses a piston 21 integrated with the piston rod 17. The cylinder housing 20 may be coated with a material such as PTFE (polytetrafluoroethylene), ENP (electroless nickel plating), or HCR (hard chromium plating). The present example describes a double-acting type, and the cylinder part 19 is provided with air inlet and outlet ports 38 and 39. In accordance with the inlet / outlet of compressed air into air chambers 22a and 22b via these air inlet / outlet ports 38 and 39, the piston 21 performs a reciprocating motion, and the piston rod 17 correspondingly performs a linear reciprocating motion. This movement is transmitted via the pin rollers 16 to the Scotch-Yoke crank drive 35 and converted into a rotary movement.

[0075] The Scotch-yoke crank mechanism 35 is provided with a rotating shaft that can be pulled out via a fixed part 23 and inserted into a keyway (not shown). The rotation of the rotating shaft is transmitted to the Scotch-yoke crank mechanism 35 via the fixed part 23.

[0076] The rotary shaft of the present example is driven by the output shaft 14 on the side of a ball valve 3 (bottom in Fig. 3) and the control shaft 4 on the opposite side (top in Fig.3). The output shaft 14 and the control shaft 4 are inserted into the housing 18 via cylindrical members 24 and 25, respectively. A predetermined bearing is fitted into a metal shaft bearing (not shown) for the cylindrical members 24 and 25. These cylindrical members 24 and 25 are each fitted into a bearing part formed in the housing 18, and the output shaft 14 and the control shaft 4 are inserted therein. The rotary shaft is rotatably and pivotably mounted on the main body of the actuator 2.

[0077] It should be noted that, in accordance with the implementation, a pressure sensor (not shown) may be provided to the actuator 2 accordingly. In this case, for example, a speed controller (not shown) is provided at each of the air inlet / outlet ports 38 and 39, and a pressure sensor is connected between these air inlet / outlet ports 38 and 39 and the speed controller via a coupling such as a T-piece, nipple pipe, or the like. In this case, the pressure sensor is inserted into a branch portion of the T-piece, so that the inlet / outlet of compressed air is not affected, and pressure measurement can be performed by a pressure sensor with a simple structure.

[0078] The state detection target by the system of the present invention is a valve, and in the present invention, a rotary valve that opens and closes a flow path by rotating the valve stem. The valve stem is formed by the output shaft 14 and the control shaft 4 of an automatic valve via the actuator 2. However, the valve stem as the target is not limited to any of the automatic valves and, although not shown, may be a rotary shaft formed from the stem of a manual valve via a handle. Although the rotary valve of the present example is a quarter-turn ball valve, the target may be any of various rotary valves, including motor-driven types such as a plug valve, a butterfly valve, or a 180-degree rotation ball valve.

[0079] The ball valve 3 from Fig. 1 to Fig.3 is a floating-type ball valve, and a valve box is configured with a body 26 having a primary flow path 26a and a body cover 27 having a secondary flow path 27a, which are fixedly connected with bolts / nuts 28. A flange is formed on each of the body 26 and the body cover 27 at a connecting part of the flow paths 26a, 27a.

[0080] The ball 30, which is a valve body, is of a full-bore type having a substantially spherical section and a passage 30a shaped to have the same diameter as the flow paths 26a and 27a, and is supported by two annular ball seats A1 and A2, which are valve seats, from a primary side and a secondary side in the valve chamber. The fastening of the ball 30 by these ball seats A1 and A2 is adjusted by the fastening of the bolts / nuts 28. At an upper end portion of the ball 30, an engaging portion 29 (e.g., convex-concave engaging portion) is formed with which the stem 15 (valve stem) can be engaged. Via this engaging portion 29, a rotational motion of the ball 30 is transmitted to the stem 15 with high accuracy.

[0081] The stem 15 is rotatably mounted on a stuffing box 31 of the body 26 via a cylindrical stem bearing B. Furthermore, a stuffing box packing C and a sealing washer are press-fitted between the stem 15 and the stuffing box 31 by a packing retainer 32. The fastening of the packing retainer 32 is adjusted by tightening a retaining screw 33. A bracket 34, which is a coupling element between the main body of the actuator 2 and the ball valve 3, is fastened with screws 40. Furthermore, a rectangular connecting part (not shown) is formed at a lower part of the output shaft 14. A fitting part (not shown) formed at an upper part of the stem 15 is attached to this connecting part to couple the output shaft 14 and the stem 15, thereby transmitting a rotary motion of the output shaft 14 to the stem 15 with high precision.

[0082] In Fig.In FIG. 1, a rotary encoder 37 indicated by dashed lines is pre-attached to the target product prior to condition monitoring by the system of the present invention to obtain necessary data for use in the present invention, and is fundamentally not assumed to be used in an actual usage scenario of the present invention. The rotary encoder 37 of the drawing is connected to an upper end portion of the control shaft 4 via a substantially C-shaped fixing plate 36 to accurately measure at least one rotational speed of the control shaft 4, and the measurement data is appropriately recorded as unique data of the target product. In the present example, a product "E6C3-C" manufactured by OMRON Corporation is used.

[0083] Next, a basic method of use in the valve condition monitoring system of the present invention will be described. The monitoring unit 1 can be mounted, as needed, in a location where the target product (valve or actuator) can be easily placed, for example, the monitoring unit 1 is mounted in a location where the unit can be left for an extended period of time without hindering the operation of the target product. Although the above-mentioned Fig. 1 and Fig. 2 is not restrictive, the unit must at least be mounted in such a way that it rotates exactly with the rotation of the control shaft 4 (valve spindle).

[0084] If the unit in the Fig. 1 and Fig.2, a bolt hole of the fitting 5 is aligned with the female screw part provided at the upper end of the control shaft 4 in the NAMUR standard, and when the state of the fitting 5 is aligned in a suitable fastening direction, the unit can be fixed only by screwing in the bolt 6. In this way, the monitoring unit 1 of the present invention can be easily retrofitted to a predetermined position of the target product without removing the existing actuator 2 and / or valve 3 from the pipeline, or removing the actuator 2 from the valve 3, or without adjusting the existing instrumentation or the like at all. After placement in this way, the rotational movement characteristic of the control shaft 4 can be accurately detected.

[0085] Furthermore, the above-described mounting method reduces external protrusion to prevent expansion of the installation space. Thus, the unit can also be connected to an automatic valve installed in a narrow space. The monitoring unit 1 can also be mounted in a position offset by 180 degrees from the actuator 2, and in this case, it can be mounted simply by attaching and detaching the bolt 6 in a similar manner to the above. This allows the monitoring unit 1 to be mounted on either side 180 degrees opposite to the installation situation of the valve 3 and / or the actuator 2.

[0086] Furthermore, the monitoring unit 1 is mounted on the control shaft 4 in a correspondingly positioned position not only when the valve 3 is fully open, but also when the valve 3 is at a mid-opening position and the control shaft 4 is rotating. Thus, the unit can be precisely mounted even during operation of the automatic valve to enable initial adjustment work.

[0087] After attaching the monitoring unit 1, the operating status of the valve 3 can be visually detected at any location using the portable terminal 44. At this time, with the help of the Bluetooth communication module 10 (registered trademark), even if the valve 3 and / or the actuator 2 are installed in a complex pipeline or in a narrow space without direct visual detection, a check can be performed from a nearby location using the portable terminal 44.

[0088] When an initial setting mode function is applied in advance to perform initial setting work on the portable terminal 44 immediately after the installation of the monitoring unit 1, it is only necessary to appropriately reset to the initial setting mode state corresponding to the usage mode of the monitoring unit 1. In this case, for example, data such as angle data is set to an initial value in accordance with a fully closed state of the valve 3. Also, at this time, no setting work is required on a target product side such as the actuator 2 and / or the valve 3, and the setting can be performed, for example, using the product information and / or the order number stored in the IC tag 12. Also, for example,Application software for downloading for portable terminals 44 is downloaded from the URL and initial setting data is transmitted to the server, whereby an installation date of the monitoring unit 1 can be recorded.

[0089] After the initial setup work is completed, this initial setup mode is switched to a normal mode. As described above, at the time of switching to the normal mode, the power supply 11 may be turned off after a predetermined time has elapsed to transition to a power-saving mode.

[0090] On the other hand, as the portable terminal 44 described above, for example, a smartphone (not shown), a tablet 44, or the like is used. In this case, the portable terminal 44 has, as functions related to data input, for example, (1) a function of receiving data and unique information from the monitoring unit 1, (2) a function of transmitting the data and unique information received from the monitoring unit 1 to the server (not shown), and (3) a function of storing GPS (Global Positioning System) position information, camera images, etc., and transmitting them to the server.

[0091] In (1) the function of receiving data and unique information from the monitoring unit 1, the angular velocity data at the time of rotation of the control shaft 4 is received via the communication module 10. On the other hand, the unique information of the actuator 2 and / or the valve 3 is received via the IC tag 12.

[0092] In (2), the function of transmitting the data and unique information received from monitoring unit 1 to the server is used, e.g., a wireless communication module (not shown) in the middle array, such as LTE (Long Term Evolution) or Wi-Fi, and each of these modules transmits the data to the server. In this case, the measurement data is not processed.

[0093] (3) The function of storing GPS positioning information, camera images, etc., and transmitting them to the server is an optional function. With this function, an image captured by the camera of the portable terminal 44, indicating the status of the actuator 2, is transmitted to the server.

[0094] On the other hand, the portable terminal 44 has, as functions related to data output using the portable terminal 44, for example, (1) a function of causing the information received from the server to be displayed based on the data transmitted to the server, and (2) a function of causing information such as a preliminary abnormality report determined by the application software based on the information received from the monitoring unit 1 but not via the server to be displayed.

[0095] Although not shown, in (1) the function of causing the display of the information received from the server based on the data transmitted to the server, information including at least diagnostic results of the valve and / or actuator can be easily displayed in a visually recognizable form. For example, with angular velocity data corresponding to the measured opening / closing count of the valve in a range from fully open to fully closed (fully closed to fully open) displayed together with comparison target data in a graph, and then the determination results are displayed.In addition, it is possible to display each of the following items: the number of operations, the operating time, the pressure data and the history of the operating torque, the fluid pressure and temperature, the ambient temperature and humidity of the actuator 2, and furthermore, drawings of the actuator 2, the valve 3 and so on.

[0096] In addition, as other functions, information on maintenance recommendations based on the history described above, etc., may be displayed, or when there is a suspicion of erroneous input in the initial setting of the monitoring unit 1 or the target product with the monitoring unit 1 attached to it is an imitation, a notice may be displayed. Examples of this case include those where the actuation time of the actuator 2 is extremely fast or slow for the model type and order number (specific specifications vary for each order) of the actuator 2 and / or the valve 3 entered in the IC tag 12, or the image of the actuator 2 on site captured by the camera of the portable terminal 44 is very small.

[0097] On the other hand, (2) in the function of prompting the display of information such as a preliminary abnormality report determined by the application software based on information received from the device but not via the server, for example, when the actuation time is extremely long or when the value of the gyro sensor 7 remains unchanged even though air pressure is applied, that is, when the actuator 2 is not activated, a determination is made as an abnormality and displayed as a preliminary report. Furthermore, when such an abnormal value is measured, an indication of data transmission to the server is displayed.

[0098] Next, the system as a server for use in the above-described system has (1) a function of accumulating unique information of the actuator 2 and / or the valve 3, (2) a function of accumulating measurement data of the angular velocity data and the air pressure of the actuator 2, (3) a function of calculating the operating torque of the actuator 2, and (4) a function of transmitting to and receiving from the portable terminal 44.

[0099] In (1) the function of accumulating unique information of the actuator 2 and / or the valve 3, the server accumulates drawing information and design information of the actuator 2 for use in calculating the activation torque. In (2) the function of accumulating measurement data of the angular velocity data and air pressure of the actuator 2, when the server receives measurement data from the portable terminal 44 multiple times, these pieces of measurement data are accumulated as a series. In (3) the function of calculating the actuation torque of the actuator 2, this is calculated based on the air pressure data received from the portable terminal 44, for example, from the cylinder diameter of the actuator 2, the offset amount from the center axis of a pinion (or Scotch yoke crank mechanism), the conversion efficiency, etc., which are not shown.In (4) the function of transmitting to and receiving from the portable terminal 44, transmission to and reception are performed by a mid-field wireless communication module such as LTE or Wi-Fi.

[0100] It should be noted that although the example in the above embodiment was described in which an air pressure type actuator is used as the actuator for automatic operation, a fluid pressure type actuator other than the air pressure type or a motorized actuator may be used. The housing of the fitting 5 and the monitoring unit 1 can be changed to conform to its external shape according to the size of the valve 3 and / or the actuator 2. Furthermore, although the control shaft 4 is provided according to the NAMUR standard in the above embodiment, it may also be provided according to another standard. In this case, too, the shaping is performed according to the shape, so that installation to the actuator is easy to retrofit as with the NAMUR standard.

[0101] Here, examples in which a ball seat in a 90-degree rotation floating ball valve is diagnosed will be described below. However, the system of the present invention is not limited to this objective, and detailed diagnosis can be performed at the level of a specific part / symptom of the target product by analyzing the shape and pattern of a characteristic diagram (angular velocity diagram) generated from data including angular velocity data collected on a large scale from the target product. Specifically, with a valve as the target part / component, it is suitable to detect at least the wear of the valve seat, the gland packing C, and / or the stem bearing B.

[0102] As in Fig. 5 to Fig. 9 and Fig. 15 to Fig.23, which are angular velocity diagrams of the examples, at least several peak values ​​are indicated in each angular velocity diagram. Such an opening degree or time evolution diagram including these peak values ​​cannot be detected by, for example, a normal angle (position) sensor provided for a rotary valve. Therefore, with conventional techniques, the system cannot be configured as in the present invention to make a detailed diagnosis based on the information about these peak values ​​(such as positions, values, and widths of the peaks in the diagram). After careful studies by the applicant of the present application, it has been found that such an angular velocity diagram can be detected at least by the gyro sensor 7 as described above.

[0103] This is taken into account, at least in the case of an oscillating gyro sensor made of a semiconductor type manufactured with MEMS, in the measurement principle as follows. That is, since a normal angle sensor can only measure a discrete angle for each duration, calculating it as a gradient within a duration in a time history diagram is the only way to convert angle data into angular velocity. In contrast, in a gyro sensor, an instantaneous Coriolis force detected by an oscillating element is converted into an angular velocity for measurement. Thus, depending on the setting, an essentially actual angular velocity can be accurately measured. Furthermore, achieving this with an angle sensor requires, at the very least, setting an extremely short duration, which is impractical.

[0104] In this respect, there is not much difference between the two (angular velocity data acquired by the angle sensor and that acquired by the gyro sensor) for smooth, slow, and continuous movements. However, there is a difference between the two for rotational movements of a target that moves due to fine, random, and discontinuous friction when receiving an action, e.g., the valve stem of a rotary valve. In particular, in an angular velocity diagram acquired by the angle sensor, fine movements cannot be tracked in detail, and therefore a non-curved, oscillating pattern such as peaks cannot be detected. However, the gyro sensor can well detect fine movements of the valve stem due to the action of friction. Therefore, there is the possibility of acquiring a precise angular velocity diagram with the appearance of peaks at a variety of points.

[0105] In addition, inertial sensors, which are typical of the sensors of the internal information type, are usually classified into acceleration sensors and gyroscopic sensors. In conventional technologies, there are also valve opening sensors of a type that incorporate this acceleration sensor and can be easily mounted on an upper end part of the spindle of the rotary valve. That is, the rotation angle or the like of the valve handle is detected through this acceleration sensor or the like. However, although at least a MEMS-type acceleration sensor, which has been widely used in recent years, is in principle excellent for detecting a translational or vibrating motion or a gradient with respect to the direction of gravity, the detailed detection of a rotational motion is not impossible, but there is much room for improvement in detection with a simple structure.

[0106] Such an acceleration sensor has the property that motion in a horizontal plane without a gradient relative to the direction of gravity is almost in a dead zone, making detection of such motion extremely difficult. Furthermore, the acceleration sensor easily picks up unnecessary components other than rolling acceleration, such as gravitational acceleration components and translational (vibrational) acceleration components. Furthermore, it has been theoretically proven impossible to adequately separate the measured unnecessary acceleration from an output signal using at least one acceleration sensor.In practice, this type of valve opening sensor has limitations in the vertical orientation and direction of a connection target. In most cases, the sensor configuration is adjusted for use according to this target after confirming the vertical orientation of a valve as a connection target. Therefore, it is difficult to capture in detail the rotational motion that occurs when receiving random friction, even with a simple structure consisting only of an acceleration sensor. It should be noted that... Fig. 31 and Fig. 32, which are described below, represent an actual verification in which data regarding a rotary movement of a rotary valve cannot be collected in detail depending on the acceleration sensor.

[0107] As specifically described in the following examples, the structure unique to the target product (ball valve) with the monitoring unit attached thereto is associated with the positions, sizes, and peak width of a plurality of peaks present in angular velocity data, which may be presented in graph form, to perform precise condition detection and diagnosis of the target product based on these detected details. Examples

[0108] Fig. 5 to Fig. 23 each show an implementation example when the state of the valve is detected from the angular velocity data. Fig. 5 to Fig. 9 and Fig. 15 to Fig.20 are examples of an angular velocity diagram detected by the gyro sensor 7 using the monitoring unit 1 of the present invention when the ball 30 is rotated in the quarter-turn actuator 2 described above and the Fig. 1 to Fig. 3 is rotated 90 degrees from the fully closed to the fully open state, with the angular velocity indicated on the right vertical axis (unit: degrees / second). These angular velocity measurements also show measurements in the Y-axis direction in the Fig. 1. It should be noted that although the measured values ​​in the X-axis and Z-axis directions are not used as diagram data in this example, they can be used additionally for the purpose of correcting a mounting error of the gyro sensor.

[0109] The transverse axis in each drawing indicates a valve actuation time, which is the time after air pressure is supplied to actuator 2 via a speed controller (unit: millisecond). Specifically, the valve is a stainless steel ball valve with a nominal diameter of 50 A and a nominal pressure of 20 K. The diagnosis targets are the PTFE+PFA ball seats A1 and A2, the glass fiber-impregnated PTFE stem bearing B, and the V-PTFE packing C (the ball seats A1, A2, the stem bearing B, and the stuffing box packing C are collectively referred to as “wear parts”). As can be seen from the opening / closing count shown in the drawings, Fig. 5 the data after zero times opening / closing, in Fig. 6 after thirty times, in Fig. 7 after five hundred times, in Fig. 8 after a thousand times and in Fig. 9 after ten thousand times. Also Fig. 15 and Fig.18 show data collected after zero opening / closing, Fig. 16 and Fig. 19 after five hundred times and Fig. 17 and Fig. 20 after fifteen hundred times.

[0110] Furthermore, in the present examples, the Fig. 1 is attached to the control shaft 4 together with the monitoring unit 1, and the angle data detected by this encoder 37 is also shown on the left vertical axis in each drawing as the valve opening degree in Fig. 5 to Fig. 9 and Fig. 15 to Fig. 20 (unit: degrees).

[0111] Fig. 10 to Fig. 14 show schematically a complete opening to a complete closing of the Fig. 5 to Fig. 9 and Fig. 15 to Fig.20 in this order of figure numbers and are particularly drawings for describing the positional relationship between the passage 30a of the ball 30 and the ball seats A1 and A2, etc. Fig. 10 shows an opening degree of 0 (fully closed), Fig. 11 shows an opening degree of approximately ten degrees, Fig. 12 shows an opening degree of approximately twenty degrees, Fig. 13 shows an opening degree of approximately eighty degrees and Fig. 14 shows an opening degree of ninety degrees (fully open). It should be noted that Fig. 10 to Fig. 14 each a sectional view along the line BB in Fig. 3 correspond.

[0112] Even if the condition in Fig. 10 is assumed to be 100%, the contact ratio between the ball 30 and the ball seat A1 or A2 in Fig. 11 still 100%, is in Fig. 12 to 85%, is in Fig.13 further reduced to 62% and in Fig. 14 returned to 100%. [Table 1] TEST NO. ACTIVATING TIME (SECONDS / 90 DEGREES) ALIGNMENT OF THE MOUNTING ACTUATOR SUPPLY PRESSURE FLUID FLUID PRESSURE (MPa) PIPELINE SUPPORT AMBIENT TEMPERATURE (°C) 1 8 LATERALLY 0.4 AIR 0.55 10 30 2 2 LATERALLY 0.5 AIR 1 70 60 3 4 VERTICAL 0.5 AIR 1 10 0 4 8 VERTICAL 0.6 WARM WATER 0.1 70 0 5 2 VERTICAL 0.5 WARM WATER 0.55 40 30 6 4 HORIZONTAL 0.4 WARM WATER 0.55 70 60 7 2 HORIZONTAL 0.4 COLD WATER 0.1 10 0 8 8 HORIZONTAL 0.5 COLD WATER 1 40 60 9 4 LATERALLY 0.6 COLD WATER 0.1 40 30 10 4 HORIZONTAL 0.5 STEAM 1 100 25 11 2 HORIZONTAL 0.2 COLD WATER 0.1 10 0

[0113] Ten test conditions in Table 1 are examples of test product conditions that are at least required from the quality engineering perspective for testing the system of the present invention. Fig. 5 to Fig. 9 show data from tests under the conditions of Test No. 10 in the table (however, the nominal pressure of the valve to be used was 10 K), Fig. 15 to Fig. 17 show data from tests under the conditions of Test No. 2 and Fig. 18 to Fig. 20 shows data from tests under the conditions of Test No. 8 in the table.

[0114] In Table 1, the actuation time is a setting time of a speed controller for controlling a 90-degree rotation of the valve from fully closed to fully open, and the orientation of the fixture is the orientation of the valve with respect to the piping, where horizontal is an orientation in Fig. 1 with a bottom in the drawing as floor, vertically an alignment in Fig. 2 with a bottom in the drawing as the bottom, laterally an alignment of the pipes with the axial center of the flow path in Fig.1 rotated 90 degrees around the rotation axis. In addition, the actuator supply pressure indicates an air pressure (MPa) to be supplied to the actuator, and the fluid indicates the type of test fluid. The fluid pressure indicates the pressure of the fluid, the piping support indicates the distance (cm) from the flange position of the valve to a piping support section connected to the valve, and the ambient temperature indicates the temperatures of a test environment. In addition, tests of Test Nos. 1 to 9 are conducted in a bath at constant temperature and constant humidity, and a test of Test No. 10 is conducted indoors.

[0115] In the following, using each of the individual angular velocity diagrams in Fig. 5 to Fig. 9 (Test No. 10), Fig. 15 to Fig. 17 (Test No. 2) and Fig. 18 to Fig. 20 (Test No. 8) with reference to the Fig. 10 to Fig. A general overview of the method for performing valve condition monitoring is provided based on the valve opening degree situations illustrated in Figure 14. In this method, particular attention is paid to detecting a wear condition of the ball seats A1, A2 as a valve condition. It should be noted that the diagnostic process described below can be physically and logically incorporated into the system of the present invention after being specifically implemented as information processing (a series of processing steps) capable of utilizing computer hardware resources.

[0116] Also, the prediction of valve failure and service life using angular velocity data by the system of the present invention can be acquired from a rotational movement of the valve from fully open or fully closed to fully closed or fully open with respect to the valve opening degree described above, i.e., from transitions of angular velocity data corresponding to the entire complete strokes of the valve, or from transitions of angular velocity data corresponding to a part of the strokes, e.g., ranges of valve opening degrees that are characteristic, such as ranges T1 to T3, as described below. In addition, as other data usable in the system of the present invention, e.g., the operating state of the valve in a plant or building facility, or angular velocity data in a state of checking the function of the valve (a so-calledPartial stroke test) can be used.

[0117] First of all, Fig. 5 to Fig. 9 (Test No. 10), the range T1 is a range in which the degree of opening of the valve, measured by the rotary encoder, ranges from a fully closed state to an opening degree of approximately 10 degrees. The function of the ball 30 corresponds to the conditions in Fig. 10 to Fig. 11.

[0118] Within this range T1 (a range in which the angular velocity frequently goes up and down in a state where the ball 30 forms a contact seal with the entire circumference of each of the ball seats A1 and A2), the ball seats A1 and A2 are both in a state of contact with the ball 30, and this state corresponds to a state immediately after a transition from static friction to dynamic friction. Regarding the frequency of the decrease in the angular velocity in this range, the frequency in Fig.6 can be read twice, but is in Fig. 9 is increased fourfold. For example, this data characteristic can be used to estimate that a certain difficulty occurs in the rotation of the ball 30, in which the ball 30 is moved to a secondary side A2 of the ball seat, e.g., due to wear of the ball seats A1 and A2, increasing a compressive force and a dynamic friction force. This can therefore be used, for example, to predict failure due to wear of the ball seats A1 and / or A2 or deterioration of the seal surface.

[0119] A time change until the range T1 is reached, ie the time required from the moment at which air pressure is supplied to the actuator 2 until the ball 30 begins to rotate, can also be used to predict failure. Specifically, Fig.6, the range T1 starts at a time close to 1000 milliseconds, and a time delay from the air pressure supply to actuator 2 until rotation is in the order of one second. In contrast, it starts in Fig. 8 at a time point close to 350 milliseconds. As the valve's open / close count increases, this time delay decreases. From these data characteristics, it can be estimated that the static friction force of the ball seats A1, A2 decreases.

[0120] In addition, the duration of the T1 range can also be used for failure prediction. While the T1 range in Fig. 7 with about 1000 milliseconds to 1800 milliseconds, it is in Fig.8 between approximately 350 milliseconds and 1500 milliseconds. As the valve's open / close count increases, the time required for the T1 range increases, meaning the time required for the rotation of the ball 30 increases. From this data characteristic, it can be estimated that an increase in the dynamic friction force of the ball seats A1, A2 is occurring. This can therefore be used, for example, to predict failure due to abrasion of the ball seats A1, A2.

[0121] In Fig. 5 to Fig. 9, the area T2 is a small band with an opening degree of about 30 degrees, and this corresponds, as the operation of the ball 30, to a state in which the ball continues to rotate (opening degree of about 20 degrees to 30 degrees), close to approximately the state in Fig.12. In the vicinity of this region T2, a state is included in which, from a state of contact of the entire circumferential surface in the region T1, the passage 30a of the ball 30 reaches the ball seats A1, A2 to make a transition to a state of partial contact, and upon valve opening, the fluid pressurizes the inner wall of the passage 30a of the ball 30 to apply a force in the valve opening direction to the ball 30. In the present example, the fluid flows in Fig. 10 to Fig. 14 from left to right.

[0122] This effect of the valve opening force by the fluid is also recorded in the angular velocity diagram (characteristic curve) as a steep increase. More precisely, while the local maximum value near the area T2 in Fig. 6 can be read as approximately 44 degrees / second, the local maximum value in Fig.8 in the vicinity of the same is approximately 63 degrees / second. Thus, an increase in the local maximum value of the angular velocity with the increase in the opening / closing count of the valve can be read. According to this data characteristic, it can be estimated that a decrease in the dynamic friction force of the ball seats A1, A2 occurs. This can be used, for example, to predict failure due to abrasion of the ball seats A1, A2. In a state where the ball 30 partially comes into contact with the ball seats A1 and A2, in addition to a decrease in the dynamic friction force, the decrease in the dynamic friction force progresses, with a force exerted by the action of the fluid pressure on the rotation direction of the ball 30. Thus, the fluid pressure is detected as a friction element corresponding to the area T2.

[0123] As with the T1 range, a time until reaching the T2 range can also be used for the failure prediction. While the T2 range in Fig. 6 occurs at about 2300 milliseconds, it occurs in Fig. 8 occurs at approximately 2000 milliseconds. Therefore, the time decreases as the valve's opening / closing count increases, and rotation begins at an early stage. Therefore, it can be estimated that the static friction force or dynamic friction force of the ball seats A1, A2 decreases, and this can be used to predict failure when the ball seats A1, A2 wear out.

[0124] In Fig. 5 to Fig. 9, the range T3 is a range from an opening degree of about 80 degrees to a fully open state (an opening degree of 90 degrees and an angular velocity of 0), and this corresponds, as the operation of the ball 30, to a state of Fig. 13 to Fig.14. This region T3 includes a state in which, with respect to the ball seats A1, A2, a transition again occurs from the state with partial contact to the state with full circumferential surface contact and a transition from the dynamic friction force to the static friction force occurs.

[0125] While in the T3 area in Fig. 6 a tendency is indicated in which the magnitude of the angular velocity decreases from about 42 degrees / second, in Fig. 8 indicates a decreasing trend of approximately 30 degrees / second. According to this data characteristic, even a transition to the state in which the ball 30 seals the entire circumferential surface of the ball seats A1 and A2 does not lead to a decrease in angular velocity. Thus, it can be estimated that, for example, a decrease in the dynamic friction force occurs, and this can be used to predict failure due to abrasion of the ball seats A1, A2.

[0126] The duration of the T3 range can also be used for failure prediction. While the T3 range in Fig. 7 with approximately 3500 milliseconds to 4000 milliseconds, it is in Fig.8 between approximately 3500 milliseconds and 4100 milliseconds. As the valve open / close count increases, the time required for the range T3, that is, the time required for the rotation of the ball 30, increases. From this data characteristic, it can be estimated that an increase in the dynamic friction force of the ball seats A1, A2 occurs. Thus, this can be used, for example, for failure prediction when the ball seats A1, A2 wear out. It should be noted that although the present example exemplifies the state in which the required time increases with the increase in the valve open / close count in the range T3, this is not meant to be limiting, and the wear state of the ball seats A1 and A2 can be detected with respect to a state in which the required time decreases.

[0127] Next, the results of measuring an actual wear amount of the ball seat A2 are described using the above-described test No. 10 as an example. It should be noted that Fig. Figure 24 is a schematic descriptive diagram describing this measurement situation. During the measurement, after operation at each of the opening / closing counts (thirty times, five hundred times, one thousand times, and ten thousand times), the ball valve 3 was disassembled to extract the ball 30 and the secondary ball seat A2, and, as shown in the schematic diagram of Fig.24, the extracted ball seat A2 was placed on a suitable horizontal surface with the extracted ball 30 placed on its sealing surface, and a total height h was measured from one side of the bottom of the ball seat A2 to the apex of the ball 30 at each of the opening / closing counts. That is, according to an increase in the amount of wear of the ball seat A2, this total height h decreases slightly, so that at least the degree of a wear condition can be detected from this decrease amount (the total height h is referred to as the "G dimension" of the ball seat A).

[0128] In fact, the amount of reduction = 0.26 mm was between thirty times (corresponding Fig. 6) and five hundred times (corresponding to Fig. 7) Opening / closing count is the same. However, the amount of decrease is 0.36 mm for the thousand times opening / closing count (corresponding Fig.8) and the amount of decrease = 0.48 mm for ten thousand times the opening / closing count (corresponding to Fig. 9), and it was thereby confirmed that the decrease amount indeed increases with increasing actuation count and wear. It should be noted that as a result of checking the actual seal surface by visual inspection after actuation, at each of the opening / closing counts of zero times and thirty times, little change was observed on the seal surface, but a linear or groove-shaped contact trace was observed at the opening / closing count of 1,000 times, and a sign of abrasion with a metal (ball) and a band-shaped contact trace was observed at the opening / closing count of 10,000 times.

[0129] It should be noted that in this example, valve seal leakage was confirmed after ten thousand actuations. Therefore, the acquisition of at least the angular velocity data in Fig. 9 a failure prediction due to wear of the ball seats, etc., as well as a service life prediction can be carried out.

[0130] Next, a general overview of the condition monitoring process in Fig. 15 to Fig. 17 (Test No. 2) and Fig. 18 to Fig. 20 (Test No. 8). Also in each of the Fig. 15 to Fig. In Figure 20, the areas T1 to T3 show the same meaning, similar to that described above. From these diagrams, the valve status can be monitored, as with the previous ones.

[0131] This means that by reading the time until the region T1 is reached, the duration, or a change in the frequency of occurrence of a peak of the local maximum or local minimum of the angular velocity in this region in accordance with the valve's open / close count, at least the wear condition of the ball seats can be inferred and used for failure prediction. In the region T2, by reading a change in the position or size of the peak of the local maximum in accordance with the valve's open / close count, at least the wear condition of the ball seats can be inferred and used for valve failure prediction. Fig. 16 and Fig.However, in test No. 17 (Test No. 2), the position of the peak of the local maximum deviates from the other results and may be shifted to a position near a region T2' (a small band with an opening degree close to approximately 40 degrees). Also in the T3 region, by reading a change in the time and duration to reach this region or the rate of change of the angular velocity in this region in accordance with the opening / closing count, at least the wear condition of the ball seats can be inferred and used for valve failure prediction.

[0132] Next, in each case Fig. 21 to Fig.23 (Test No. 11), although no structural diagram is shown, shows an example of an angular velocity diagram detected by the gyro sensor using the monitoring unit of the present invention when the valve body rotates 90 degrees from the fully closed to the fully opened state in a double-acting pneumatic actuator with a rack-and-pinion design and a quarter-turn butterfly valve. The details of the curve marking are similar to those described above, and the test conditions are the same as those of Test No. 11 in Table 1.

[0133] Specifically, this butterfly valve has a die-cast aluminum center-flap valve structure with a nominal pressure of 10 K and a nominal diameter of 50 A. The monitoring unit of the present invention is mounted on its valve stem in a manner similar to that described above. The diagrams in the drawings are also similar to those described above, and the angles measured by the rotary encoder and the angular velocities detected by the gyro sensor built into the monitoring unit (Y-axis readings) are shown in graphic form. The diagnostic target is a rubber seat made of EPDM. In addition, Fig. 21 data is shown after opening / closing with the open / close count value of zero, in Fig. 22 after five hundred times and in Fig. 23 were recorded after one thousand five hundred times.

[0134] Also in Fig. 21 to Fig.23, each of the areas T1 and T2 means the same as above. Based on these diagrams, valve condition monitoring can be performed in a similar manner to that described above. That is, the area T1 is an area where the valve body leaves the state of contact with the rubber seat, and is also an area where a so-called jump phenomenon occurs. In this area, when the operation is repeated with the opening / closing count value 500 times and then 1,500 times, a change in the tendency of increase / decrease of the angular velocity can be observed. According to this data characteristic, it can be used, for example, for failure prediction in cases of wear of the rubber seat and deterioration of the seal surface.

[0135] Also in the T2 range, the valve body leaves the rubber seat to align itself with a medium opening degree. In this state, an unbalanced torque from the fluid acts on the valve body, causing the valve body to open slightly wider. When the operation is repeated with the opening / closing count value of 500 times and then 1,500 times, the increase in angular velocity becomes steep, and a trend toward shortening the time to reach the T2 range can also be seen. According to this data characteristic, it can be used to predict failure in the event of abrasion of the rubber seat, for example, in the vertical direction (around the spindle) of the valve body.

[0136] Next, Fig. 25 to Fig.30 each shows an angular velocity diagram taken in another example different from the one described above. In this other example, tests were conducted under substantially the same conditions as Test No. 10 described above (conditions including horizontal piping using the method shown in Fig. 1, steam and 1.0 MPa), and also the marking of the diagrams (such as the amount shown by the individual axes and line types) is similar to that in Fig. 5 and so on. However, unlike the example described above, angular velocity data on the X-axis and Z-axis (except the roll axis) of the Fig. 1 shown gyro sensor 7. That is, Fig. 25 is a diagram in which angular velocity data in the X-axis direction at the initial movement are shown in diagram form, Fig.26 is a diagram in which angular velocity data in the Y-axis direction at the initial movement are shown in diagram form, and Fig. Figure 27 is a diagram in which angular velocity data in the Z-axis direction during the initial movement are presented in diagram form. Fig. 5 and Fig. 26 angular velocity diagrams under essentially the same conditions.

[0137] Also Fig. 28 to Fig. 30 are angular velocity diagrams after the valve has been rotated twenty thousand times after the initial movement in Fig. 25 to Fig. 27 was opened and closed, with angular velocity data in the direction of the X-axis in Fig. 28, in the direction of the Y-axis in Fig. 29 and in the direction of the X-axis in Fig. 30 are presented in diagram format, similar to the Fig. 25 to Fig. 27. Thus, Fig. 28 Fig. 25, Fig.29 corresponds to Fig. 26 and Fig. 30 corresponds to Fig. 27. In particular, it can be said that Fig. 29 data that, according to Fig. 9, i.e. a graph after the valve was opened and closed ten thousand times under essentially the same conditions.

[0138] As in Fig. 26 and Fig. 29, the angular velocity diagrams along the Y-axis show a similar trend as the other diagrams along the Y-axis. In particular, Fig. 26, as in Fig. 5, one or more peak-like features in the area T1, at least one steep pattern appears near the area T2, and a decreasing pattern appears in the area T3. Also in Fig. 29 essentially similar characteristics as described above are recorded. However, in particular in comparison to Fig.9, although the peak-like feature (maximum value) is more significant in the T2 region, captures a gradual pattern in which the angular velocity decreases overall. In any case, it can be said that easily detectable features are captured.

[0139] On the other hand, Fig. 25, Fig. 27, Fig. 28 and Fig. 30, which are diagrams with respect to directions other than the Y-axis, at least the features described above are not significant, and many random amplitudes are observed that are not easy to detect. Therefore, for angular velocity data to be presented in the form of diagrams, it can be said that angular velocity data in the rotation direction of the roll axis (Y-axis) is preferable.

[0140] Next, Fig. 31 and Fig.32 describes the system for detecting a valve state of the present invention. The present invention is directed to a system for detecting a valve state, and the system includes the valve 3, a sensor unit 1 attached to this valve 3, and a server 41 (cloud server 41) communicatively connected to this sensor unit 1. A wear state of a wear part (A, B, C) is detected based on a feature value included in measurement data measured by the sensor 7 included in the sensor unit 1 from a valve spindle 4 that opens and closes the valve 3.

[0141] In Fig. 31, valve 3 is the ball valve described above, which in Fig. 1, and the sensor unit 1 is also the monitoring unit 1 described above, which is shown in Fig. 1. As shown in Fig.As shown in Figure 2, like the monitoring unit 1, the sensor unit 1 is also mounted as a single, independent unit with a power supply 11 in a mode that can rotate with the valve spindle 4, attachable and detachable, and is connected by the communication module 10 to the server 41 via the Internet 43, and so on, using a predetermined wireless communication protocol to enable wireless communication. The ball seats A1, A2, also described above, are selected as the wear part.

[0142] In Fig.31, a tablet 44 and a PC 45 are examples of a terminal 44, 45 for checking information regarding the valve 3 to be transmitted from the sensor unit 1, and include a display capable of displaying transmission data from the sensor unit 1. For this display, any application for display, which can be obtained from an application server included in the server 41, can be used.

[0143] In Fig.31, the server 41 uses a cloud server 41. The cloud server 41 is suitable for various calculation methods and security measures described below. Furthermore, the server contains a database and a complete or partial anomaly diagnostic tool, which is not shown and is described below. Furthermore, the server may contain a predefined application server for terminal display or the like. In this case, a user with a terminal 44, 45 can access the server anytime and anywhere to view the status of a valve.

[0144] A characteristic value of measurement data for use in detecting valve states may be a time from the full opening of the valve 3 to a predetermined degree of opening, which is displayed in an angular velocity diagram ( Fig. 5 to Fig. 9, Fig. 15 to Fig. 23, Fig. 26 and Fig.29) obtained from angular velocity data in the direction of the axial center (Y-axis direction) of the valve stem 4 (e.g., a time T1 from 0 degrees until the opening degree reaches 10 degrees and a time T2 from 0 degrees to 30 degrees), a time to full closing from full opening to full closing, or a time from a predetermined opening degree to full closing (e.g., a time T3 from 80 degrees until the opening degree reaches 90 degrees). The feature value may also be the number of steep gradients and the position, magnitude, and / or width of each steep gradient of the angular velocity included in a predetermined time range (e.g., the time range T1 or T3); it may be a time until the angular velocity reaches a maximum value or a local maximum value, or the magnitude or width of the maximum value or the local maximum value, or includes all or part of them.In addition, the feature value can be a start / end point of a predetermined time (such as time T1) and, as with a leakage value, a value indicating the presence or absence of a leak (binary value). According to these types of feature values, feature data is generated as numerical data (scalars, vectors).

[0145] Here, as in Fig. 5 to Fig. 9, Fig. 15 to Fig. 20, Fig. 21 to Fig. 23 and Fig. 25 to Fig.As can be seen in Figure 30, the steep gradient in an angular velocity diagram in which the valve opening degree changes abruptly indicates a portion of one or more points at uneven positions that are skewed with respect to the time axis between full opening and full closing. A gradient to be read as a steep gradient (rate of change) can be adjusted accordingly depending on the implementation. For example, each of the following gradients can be read as a steep gradient: a gradient of a unimodal location located in the range T1 in Fig. 5 to Fig. 9, Fig. 15 to Fig. 20 and Fig. 21 to Fig. 23; a gradient close to the area T2 in Fig. 5 to Fig. 9, Fig. 19, Fig. 20 and Fig. 23; and a gradient near the T2' region in Fig. 16 and Fig. 17.

[0146] The number of steep gradients is also the number of steep gradients that appear on a graph and their readable times. The position of a steep gradient can be a time point at which this steep gradient begins or ends, a time point in the middle of these times, or, in the case of a unimodal location, a time point at a local maximum value. A displacement of a steep gradient is also a difference between values ​​(opening degrees or angular velocities) corresponding to the start and end times of this steep gradient and, in the case of a unimodal location, can be set to the peak height of a suitable local maximum value. Similarly, the width of a steep gradient is e.g.a difference between the start and end times of this steep gradient and, in the case of a unimodal location, can be set to a width corresponding to the peak height of a suitable local maximum value.

[0147] In this way, if an easily detectable feature appears in the data pattern that can be detected in accordance with the one-time opening and closing of the valve, the amount of information required for processing in the statistical processing of the data described below can be reduced or optimized. In particular, since the angular velocity diagram can be easily characterized by the gyroscopic sensor, teacher data (test data) can be easily generated as described below. With a sensor other than the gyroscopic sensor, it is difficult to detect a feature in the data pattern that can be detected in accordance with the opening and closing of the valve.Therefore, when using this information with fewer features for machine learning, it is necessary to perform statistical processing separately to extract features and utilize most or all of the acquired data. However, a characteristic steep gradient easily appears in the angular velocity diagram data for use in the present invention. Only with this limited information about steep gradients (a set of multiple numerical values ​​such as position, count, displacement, and / or width) can statistical operations be performed with high accuracy, resulting in a saving of computational resources.

[0148] Using this feature data obtained from the angular velocity diagrams, the detection of valve states in the present invention is performed by a first abnormality diagnostic means, a second abnormality diagnostic means, or a third abnormality diagnostic means in the following manner. The means that performs each function described below is not particularly restrictive and can be provided to the system in a suitable manner depending on the implementation.

[0149] In the first abnormality diagnosis means, a first reference data table (not shown) is stored in a database 42, which is formed from a plurality of label data and feature data in accordance with a predetermined opening / closing count of the valve for each specific condition, wherein the sensor unit 1 and / or the server 41 is / are provided with a first abnormality diagnosis means configured to detect a wear condition and perform an abnormality diagnosis of the valve 3, wherein this first abnormality diagnosis means comprises a specific data generation means that generates specific data formed from a specific condition of the valve 3, an opening / closing count of the valve 3, and specific feature data based on angular velocity data, a data acquisition means,which acquires, from a first reference data table, first reference data having an opening / closing count value equal to the opening / closing count value of the specific data and a nearest specific feature value, and a comparison and determination means which compares any piece of this acquired label data included in the first reference data with a predetermined threshold value to acquire a predetermined determination result.

[0150] For example, a label is dimensional data or leakage quantity data, and the label data is a numerical value of the label. In this example, the dimensional data or leakage quantity data is used as the label data. For the label, it is appropriate to use a characteristic value of a type that is important for detecting the wear condition of a wear part of valve 3.

[0151] An example of the dimension data is, for example, in the case of the ball seats A1, A2, the Fig. 24 dimension G described above, which is formed from dimensional data of any part of a wear part in the non-wearing state and decreases according to the increase in the amount of wear. The leakage amount data are, for example, in the case of valve 3 in the fully closed state, which is shown in Fig. 14, a value measured with a predetermined measuring device that measures the amount of fluid leaking between the ball 30 and the ball seats A, and is a characteristic value directly reflecting the sealing performance of the valve. The larger the leakage amount, the more the valve condition is judged to be deteriorated.

[0152] The first reference data is, for example, one record in each row specified in Table 2 below (an example of a reference data table) and consists of several combinations of labels (valve opening / closing count, dimensional wear amount of the ball seats, and the presence or absence of leakage) and a combination of feature data (start time of area T1 and local maximum value near area T2) for each specific condition and in accordance with the opening / closing count of valve 3, which includes a specific wear part. The specific conditions are various conditions required to identify a valve in a use state, such as:the valve type and the name of the product manufacturer, as well as usage conditions (such as the installation environment, including temperature and the fluid used), the type of wear part, and the part of the dimensional data. The first reference data acquires data under the same specific conditions from the valve as the measurement target, in accordance with the first reference data, including angular velocity data, and is accumulated in advance in the database 42. The accumulated first reference data is managed to be classified according to the specific condition, and a sufficient amount of data corresponding to the plurality of label combinations is acquired in advance.

[0153] Furthermore, the angular velocity data for use in at least the first abnormality diagnosis means includes data required for acquiring an angular velocity diagram, which must be measured by the gyro sensor 7, as well as information about the opening / closing count of the valve 3 and the specific condition. Furthermore, for the opening / closing count of the valve 3, if, for example, the number of measurements is defined in advance for each specific condition, records of the opening / closing count can be made uniform. Therefore, when the data acquisition means described below refers to the first reference data table, the opening / closing count of the specific data and the opening / closing count of the record of a reference can be aligned.

[0154] Therefore, as with the initial reference data, with only the sensor unit 1 attached to the valve to start, the initial reference data can be easily acquired under various specific conditions, e.g., by a valve manufacturer or maintenance company, and accumulated in the database without hindering the actual operation of the valve. Furthermore, the specific feature data means a piece of the feature data selected in advance from the feature data, and a significant characteristic value with a tendency to strongly correlate with the labels is selected. [Table 2] TARGET DRAWING VALVE OPEN / CLOSE COUNT (TIME) DIMENSIONAL ABRASION OF THE BALL SEAT (mm) VALVE SEAT SEAL LEAKAGE START TIME OF RANGE T1 (ms) LOCAL MAXIMUM VALUE OF AREA T2 (Opening degree(s)) Fig. 5 0 0 NO 800 35 Fig. 6 30 0.26 NO 1000 44 Fig. 7 500 0.26 NO 1000 45 Fig. 8 1,000 0.36 NO 350 63 Fig. 9 10,000 0.48 AVAILABLE 400 51

[0155] The specific data generation means is a means that identifies and extracts a specific feature value appearing in the graph from graph data obtained by converting angular velocity data (raw data) measured by the gyro sensor 7 into angular velocity graph data in the Y-axis direction, and combines a specific condition of the valve 3 and the valve opening / closing count at the time of this measurement, and outputs it as a set of numerical values. Note that the graph data acquired here can be output to a predetermined display device for display.

[0156] The data acquisition means is a means that uses specific data as input; accesses the reference data table of the database 42 to search for a table corresponding to a specific condition contained in this specific data; if the table matches, refers from this table to a data set in which the opening / closing count value is equal to the opening / closing count value of the valve contained in the specific data, and acquires a specific feature value (data set feature value) of a data set corresponding to a specific feature value (specific data feature value) contained in the specific data; and further determines whether this data set feature value is substantially equal to the specific data feature value. Here, a range in which they are determined to be substantially equal to each other is appropriately set in advance.

[0157] The comparison and determination means is a means that uses, as derived label data of the valve 3, a plurality of label data included in a data set having a data set feature value determined to be substantially equal to the specific data feature value, as derived label data, compares this derived label data with a plurality of threshold values ​​each set in advance for each label data, and outputs a predetermined determination result in accordance with the comparison result. For example, if the derived label data is equal to or greater than the threshold value, predetermined warning information (alarm) is output as the determination result. If the label data is less than the threshold value, predetermined information about the current state is output as the determination result. For example, ifThe safest measure is to issue an alarm when a single piece of label data exceeds the threshold.

[0158] Here, a specific reading of Table 2 is described. In a series of drawings given in the table, in a range where the opening degree of the valve is from fully closed to an opening degree of about 10 degrees (range T1), e.g., when the opening / closing count of the valve is a thousand times ( Fig. 8), the time at which the angular velocity begins to increase is earlier than in the case of five hundred times ( Fig. 7), ie 350 milliseconds, which is significantly less than 1000 milliseconds from the beginning of the valve opening process. Here, the amount of decrease in the total height (dimension h in Fig.24) of the ball seat is 0.36 mm, which is larger than the case of five hundred times (decrease amount of 0.26 mm), and it is recognized that the ball seat continues to wear.

[0159] This information is stored in advance by the valve manufacturer, the maintenance company, or the like in the memory 9, the server 41, or the like as reference data and then compared with actual measurement data (angular velocity data) of the valve 3 for use in an operating system, or the like, so that the wear condition of the ball seat in this valve can be recorded.

[0160] Specifically, in the actual measurement data of the valve with the opening / closing count of a thousand times, when the timing of an increase in angular velocity in the range T1 is 400 milliseconds, this value is close to the reference data of 350 milliseconds, and thus it can be concluded that the ball seat as a sealing element is worn almost to 0.36 mm. It should be noted that the timing of an increase in angular velocity in the range T1 is determined from only one point, 400 milliseconds, and this can be determined based on a variety of values ​​such as an average value per unit time. Although the time required for opening and closing the valve can be detected using a clock built into the CPU 8 in the present example, another separate timer may be used here.In addition, the valve's opening / closing count is counted by using a microswitch (limit switch) in addition to the rotary encoder, which detects a fully open / closed position of the valve or similar.

[0161] In a small range where the valve opening degree is approximately 30 degrees (range T2), e.g. when the valve opening / closing count is a thousand times ( Fig. 8), the value of an abruptly increasing angular velocity is larger compared to the case with five hundred times ( Fig. 7), i.e. 63 opening degrees / second (rad / s), which is significantly higher than 45 opening degrees / second.

[0162] Here, as described above, the reduction amount of the total height (dimension h in Fig.24) of the ball seat of 0.36 mm is greater than in the case of five hundred times (decrease amount 0.26 mm), and thus the progressive wear of the ball seat can be seen from the abrupt increase in the angular velocity in the region T2.

[0163] This information is stored in advance by the valve manufacturer, the maintenance company, or the like in the memory 9, the server 41, or the like as reference data and then compared with actual measurement data (angular velocity data) of the valve 3 for use in an operating system, or the like, so that the wear condition of the ball seat in this valve can be recorded.

[0164] More specifically, in the actual measurement data of the valve with the opening / closing count of a thousand times, when the angular velocity in the T2 range is 65 (rad / s), this value is close to the reference data of 63 (rad / s), and thus it can be concluded that the ball seat as a sealing member is worn by almost 0.36 mm.

[0165] In addition, by a specific reading of Table 2, in combination with valve leakage data, the service life of the sealing component can be predicted based on the measured angular velocity. If the valve's opening / closing count is ten thousand times ( Fig. 9), the reduction amount is the total height (dimension h in Fig.24) of the ball seat is 0.48 mm, which is larger than the case of 1,000 times (decrease amount 0.36 mm), and it can be seen that the ball seat continues to wear. Since valve seat leakage is confirmed in the valve, it is determined that the service life of the ball seat ends when the valve's opening / closing count reaches 10,000 times. Here, the valve seat leakage test for the valve in this example was conducted under the condition that nitrogen was used as the test fluid and the pressure of this fluid was 0.6 MPa.

[0166] This information is stored in advance by the valve manufacturer, the maintenance company, or the like in the memory 9, the server 41, or the like as reference data, and then compared with the actual measurement data (angular velocity data) of the valve 3 for use in an operating facility, or the like, whereby the service life of the ball seat in this valve can be predicted.

[0167] Specifically, for example, in the actual measurement data of the valve with the opening / closing count of a thousand times, it can be found that when the time point of increase of the angular velocity in the range T1 is 400 milliseconds or the angular velocity in the range T2 is 65 (rad / s), this is the state of the valve along the reference data in Table 2, and the service life of the ball seat ends with the opening / closing count of ten thousand times, and scheduled maintenance can be performed before the opening / closing count of the valve reaches ten thousand times.

[0168] In addition, the service life of the sealing component can be predicted based on the measured angular velocity, which involves a certain way of reading Table 2 in combination with the dimensional or consumption data that serve as a reference for the replacement of the sealing component. In particular, if the reference for replacement is such that the decrease in the total height (dimension h in Fig. 24) of the ball seat becomes 0.40 mm, it is determined based on a proportional relationship between the valve opening / closing count of thousand times and that of ten thousand times in the reference data in Table 2 that the service life of the ball seat ends when the valve opening / closing count reaches three thousand times.

[0169] More specifically, in the actual measurement data of the valve with the opening / closing count value of thousand times, when the angular velocity in the range T2 is 65 (rad / s), this value is close to the reference data of 63 (rad / s), and thus it can be concluded that a situation where the ball seat as a sealing member is worn almost to 0.36 mm, and it can be found that the service life ends with the count value of three thousand times described above.

[0170] It should be noted that, for data used in the first anomaly diagnosis tool, as shown in Table 2, feature data with two (or more) label data are accumulated in the database as test data, thus representing a so-called multi-label (multi-class classification) problem. Thus, a well-known multi-class classification learning model can be applied to the accumulated reference data.

[0171] The second and third anomaly diagnostic means perform anomaly diagnosis using a single-label machine learning scheme. A predetermined learning model generated based on labeled training data is stored in the database 42. Derived label data for use in this second anomaly diagnostic means is a derived value output from the learning model.

[0172] The learning model described above is generated, for example, as follows. Under the same specific condition, in a range where label data (dimension, leakage amount) can be considered the same, the valve is opened and closed a sufficient number of times to acquire angular velocity data, and from this, each piece of feature data is generated (i.e., a feature value is read from an angular velocity diagram). The same label data is provided to generate teacher data for training. This teacher data is sampled in sufficient quantity for each piece of label data and stored in the database 42.

[0173] Machine learning (statistical operation) is applied to a sample set of teacher data for each piece of the same label data to generate a model (identification model or generation model). While this can be considered a learning model, the investigation can be further carried out using test data, an optimal statistical model can be found, or a set of parameters for each statistical model can be adjusted, thereby increasing accuracy and reliability. Therefore, a learning model is generated using a scheme called supervised machine learning. For machine learning, selection and improvement can be done as needed according to the implementation. For example, a known scheme can be applied as needed. When the label data has continuous values, a regression scheme (such as linear regression, logistic regression, or SVM) is usually used.In this case, the learning model corresponds to a regression function f, which can be derived as “Derived label data = f (feature data)”, and the function is identified with a given parameter.

[0174] In addition, a case is conceivable where a wearing part is replaced by another part during the operation of the valve, and the label data of this other replaced part has not been sufficiently sampled in advance or does not exist at all. In this case, a learning model of the replacement part does not exist in the database, so the abnormality diagnosis tool cannot be executed. In this case, the learning model stored in the database can be corrected for use. For example, a well-known scheme such as transfer learning can be used. For example, the label data of a well-known learning model can be given a predetermined weight to correct and use the label data for the replacement part.

[0175] In contrast, the sensor unit 1 and the server 41 are equipped with an anomaly diagnosis means (not shown) configured to detect a wear condition and perform anomaly diagnosis of the valve. This anomaly diagnosis means consists of at least a feature value generation means that generates predetermined feature data, a derived label data calculation means that calculates label data (scalar) via machine learning based on the feature data, and a comparison and determination means that compares this label data with a predetermined threshold value to obtain a determination result.

[0176] The feature value generation means identifies and reads from chart data obtained by converting angular velocity data (raw data) measured by the gyro sensor 7 into angular velocity chart data in the Y-axis direction, each feature value appearing in that chart, and outputs it as a form of feature data composed of a plurality of sets of numerical values. Note that the chart data acquired herein can be output to a predetermined display device for display.

[0177] The derived label data calculation means is a means that uses feature data as input and applies this feature data to a learning model retrieved from the database 42, thereby calculating and outputting label data as a derived value. In the case of a plurality of labels (dimension value, leakage value), each learning model is retrieved according to the type of label.

[0178] The comparison and determination means takes the derived label data as input, compares this label data with a threshold value set and stored in advance in accordance with the label, and outputs predetermined warning information (alarm) as the determination result when the derived label data exceeds the threshold value, and outputs predetermined current state information as the determination result when the derived label data is less than the threshold value. If the determination results for multiple labels contradict each other, the determination result is associated with one of them as appropriate. Note that instead of this binary return (OK, NG), multiple thresholds may be set, and a determination result corresponding to the range of each threshold may be set.

[0179] For the dimensional data, for example, a first threshold may be set to a wear amount that is judged as failure (replacement required); for a wear amount less than this first wear amount, for example, data of a wear amount corresponding to a period of three months before the judgment as failure (three months before the wear amount) when a valve of the same type is used under normal use conditions may be separately acquired in advance; and this wear amount may be set as the second threshold. For example, if the value of the derived label data is equal to or greater than the second threshold and less than the first threshold, a message indicating the time three months before the required replacement may be output as the determination result.Similar to the wear thresholds (which have a smaller value as the predetermined period is longer) acquired in time series according to the amount of wear before the predetermined period (amount of wear before the predetermined period), a plurality of wear thresholds can be inserted in the value order to make the determination results more accurate. The output of these multi-level determination results can be performed similarly for leakage amount data.

[0180] It should be noted that regarding the timing of the diagnosis performed by the above-described first and second abnormality diagnosis means, a diagnosis may be performed, for example, by a user's instruction via a terminal 44, 45 or each time the valve is opened and closed. Alternatively, the timing may be set with a predetermined valve opening / closing count or at predetermined time intervals.

[0181] In addition, a means may be provided for transmitting the determination result to an application on the terminal 44, 45 so that it can be displayed, and a means for notifying a management server managed by the manufacturer of the valve (who is responsible for maintenance) of the determination result.

[0182] The teacher data (test data) using the above-described label is prepared in advance in the database 42 as a learning model for each label (characteristic value) of a wear part under the specific condition of a valve, such as a valve or a fluid used. Thus, diagnosis can be performed only by applying the feature data to this learning model. Therefore, while it is necessary to collect teacher data (test data) and generate learning models in advance, high-speed diagnostic processing can be performed during the actual operation of the valve 3, and system configuration resources can also be reduced.

[0183] Furthermore, in contrast to the above-described scheme of the abnormality diagnosis means, the valve state detection system of the present invention can also be configured according to a scheme using unsupervised machine learning. In this case, too, the database 42 can be used as a data storage device in the same form as the feature data described above. The abnormality diagnosis means of this scheme is the third abnormality diagnosis means and includes at least a data accumulation means, a data control means, a model data calculation means, an index calculation means, and a comparison and determination means.

[0184] The data accumulation means generates the same feature data as described above from angular velocity diagram data obtained from the angular velocity data measured by the gyro sensor 7, transfers this feature data to the database 42, and causes the feature data to be stored in the database 42 in a predetermined format to generate accumulated feature data. The data accumulation means may optionally use the means for converting angular velocity data into diagram data and the feature value generation means described above. This data storage is controlled by the data control means. The data control means controls the data accumulation means so that the acquired feature data is stored in the database 42 each time the valve is opened and closed until the feature data of a predetermined, preset amount is accumulated in the database 42.When the accumulated data reaches the predetermined amount, this is detected and a corresponding message is sent to the model data calculation means.

[0185] The appropriately notified model data calculation means applies machine learning to all pieces of the feature data accumulated at that time in the database 42 (accumulated feature data) to generate a learning model. An output value of this learning model is called consumption data. Therefore, the learning model is generated using a so-called unsupervised machine learning scheme. This consumption data is so-called normal data, that is, data that must be collected and accumulated during the normal operation of the valve.

[0186] In this case, too, machine learning can perform selection and improvement according to the implementation. A well-known scheme, for example, a dimensionality reduction scheme (such as PCA or SVD) is used. For example, in the subspace method, a subspace U is generated during normal operation by taking as a basis the top k vectors in a unique vector group (principal component subscripted in the distribution), which are obtained by performing principal component analysis using all parts of the accumulated feature data (used as an N-dimensional vector) at the time of normal operation. This calculation is performed by the model computation means. Thus, the learning model supports an n × k matrix (second-order tensor).

[0187] The index calculating means calculates and outputs a predetermined index defined between feature data (new feature data) by angular velocity data acquired from the initial opening / closing of the valve after the data control means notifies the model calculating means and the consumption data described above.

[0188] In the subspace method described above, a degree of anomaly (index) can be defined as a predetermined distance between the normal subspace generated by the model data calculation means and the new feature data (unknown data). For example, if a subspace U (u1 ... u) obtained from the normal data group is k ) and the unknown data x=(x1 ... x N ), an anomaly degree d 2 =x T xx T U k U T k x can be defined.

[0189] The comparison and determination means compares the above-described index with a threshold value set and stored in advance, and outputs predetermined warning information (alarm) as an abnormal outlier, for example, as a determination result when the index becomes equal to or larger than the threshold value, and outputs predetermined information about the current state as a determination result when the index is smaller than the threshold value.

[0190] Next, Fig. 32 is a general outline of a valve state detection process according to the present invention. First, the sensor unit 1 is attached to the valve 3 as a target. Specifically, the unit is mounted in the manner described above, Fig.1. Normally, the sensor unit 1 is a single, independent unit that automatically continues to monitor the valve 3 after installation, and therefore its power supply should be checked, such as sufficient charging power. In addition, the unit is usually set up to enable wireless communication, as shown in Fig. 31, and it is therefore also necessary to check a communication state with necessary communication destinations, such as the cloud server 41, the terminals 44 and 45 via the Internet 43.

[0191] In Fig.32, during the initial setting 46, the valve's opening / closing position is precisely adjusted to the gyroscopic sensor 7, and information related to valve 3 (such as the valve type and manufacturer, operating environment, and fluid used) is set in the sensor unit 1. In particular, information related to the labels (such as dimensional values, leakage amount, and threshold values) is also set. After the initial setting 46 is completed, valve 3 is actually actuated.

[0192] In Fig.32, the processes collectively designated by reference numeral 47 correspond to a general outline of the diagnosis process by the above-described first to third abnormality diagnosis means. As described above, in the first and second abnormality diagnosis means, it is necessary to store predetermined data in advance in the database 42 of the cloud server 41. Therefore, in order to execute the first and second abnormality diagnosis means, label values, that is, a sufficient number of sample data, such as a specific dimensional value of a specific wear part and a leakage amount of a specific valve under a specific condition, must be acquired in advance.

[0193] In process 47, first, chart data is acquired at a predetermined time by chart conversion means from angular velocity data measured by the gyro sensor 7 from the valve spindle 4 of the actually operating valve 3. From this chart data, feature data (a numerical value formed from a specific feature value in the first abnormality diagnosis means and a set of numerical values ​​formed from all feature values ​​in the second abnormality diagnosis means) is obtained by feature value generation means.

[0194] Next, in the first anomaly diagnosis means, specific reference data is acquired by the data acquisition means. A specific feature value contained in this reference data and a predetermined threshold value are compared by the comparison and determination means, and the determination result is provided to the user. In the second anomaly diagnosis means, a learning model is retrieved from the database 42 by the derived label data calculation means through model retrieval means, and the feature data is applied to the learning model to acquire label data. This label data is compared with a threshold value by the comparison and determination means, and the determination result thereof is transmitted to a display means (terminal device) by result transmission means, whereby the determination result can be provided to the user.

[0195] Furthermore, in process 47, the third anomaly diagnosis means can be implemented through unsupervised machine learning using the scheme described above. In this case, although it is not necessary to accumulate teacher data, a program appropriate to the product must be implemented, such as data accumulation means, data control means, model data calculation means, index calculation means, or a learning model tailored to the product.

[0196] Next, a fourth anomaly diagnostic tool is described. The configuration in Fig. 31 and Fig.32 is as described above. In the drawings, a system for detecting a valve condition comprises the valve 3, a gyroscopic sensor unit 1 attached to the valve 3 and including the gyroscopic sensor 7, and the server 41 communicatively connected to the gyroscopic sensor unit 1 and including the database 42. In the database 42, a second reference data table is stored including output data and product data corresponding to an opening / closing count of the valve 3. The gyroscopic sensor unit 1 and / or the server 41 are provided with fourth abnormality diagnosis means configured to detect a wear condition of a wear part (A, B, C) included in the valve 3 and perform an abnormality diagnosis of the valve 3. This fourth abnormality diagnosis means includes data generation means that generates measurement data including output data and product data.which are measured by the gyro sensor unit 1 in accordance with an opening / closing count value of the valve 3, a data acquisition means that acquires second reference data from the second reference data table, the output data of which of the valve 3 is substantially equal to the output data of the valve 3 included in these measurement data, and a failure determination means that determines a failure prediction of the valve 3 based on usage frequency data of the valve 3 included in these acquired second reference data.

[0197] The second reference data contained in the second reference data table includes product data and output data. Table 3 is an example of this second reference data table, where one record in each row is the second reference data. The product data is data that identifies attributes and specifications of the product and, in this example, is formed from the manufacturer name, valve type, wear component target part, and average usage frequency of the valve (usage frequency data), as follows. Regarding the output data, in this example, it is from a new product state (initial opening / closing) to a failure state (which varies for each product, e.g.Fifty thousand times) for each opening / closing (operation count), an output value of the gyro sensor for each opening degree step (1 degree → 2 degrees to 89 degrees → 90 degrees), collected in advance from the test valve with the gyro sensor attached to it, is stored in the database 42 provided on the cloud server 41 side as a reference value. This means that, for example, if the product is manufactured by the company itself, before being sold on the market, experiments with changed conditions are repeatedly conducted within the company in advance, and the results are stored as basic reference data. However, the output data may not be this data for 0 degrees to 90 degrees, but only feature parts (feature values) of the angular velocity data as described above can be partially used.

[0198] In this example, the gyroscopic sensor 7 can also output data in the same format as the output data of the second reference data, since these are included in the measurement data for each operation count. The measurement data consists of product data and output data of the gyroscopic sensor 7 for each opening / closing count (operation count) of the valve 3 and includes at least data included in the second reference data.

[0199] Note that the usage frequency data (average usage frequency of the valve) described above may also be included in the output data instead of the product data, if necessary. For example, on the gyroscopic sensor unit 1 side, the operation count of the valve 3 in operation may be acquired at a predetermined time, and the usage frequency may be calculated based on this operation count and output as included in the output data. If the monitoring unit 1 (sensor unit 1) is connected to the valve 3 during use and information about the operation count of the valve 3 at that time has been acquired in advance, this operation count may be input to the monitoring unit 1 (sensor unit 1) to correct the operation count in the output data. [Table 3] PRODUCT DATA OUTPUT DATA NAME OF THE MANUFACTURER VALVE TYPE TARGET PART AVERAGE VALVE USE FREQUENCY (TIME / MONTH) OPERATION COUNT ANGLE (degrees) Angular velocity (degrees / s) OWN COMPANY BALL VALVE A BALL SEAT D N1 NEW (FIRST TIME) 0 y1 1 y2. . . . . . . 89 y89 90 y90 (SECOND TIME) 0 y1 1 y2. . . . . 90 y90 FAILURE (FIFTY THOUSAND TIMES) . . 0 Y1 . . . . 45 Y45 OWN COMPANY BALL VALVE A STUFFING BOX PACKING E N1 NEW (FIRST TIME) 0 y1 90 y90 . . . CANCELLED 0 Y1 (SIX-TRAFFIC- . . SENDMAL) 37 Y37 ANOTHER COMPANY BALL VALVE B BALL SEAT F N2 NEW 0 *** (FIRST TIME) . *** . *** 90 *** . . *** CANCELLED . ***

[0200] The data generation means is a means that generates, as a piece of measurement data, measurement data (full opening degree data of angular velocity) about one revolution from fully open to fully closed, which is measured by the gyro sensor 7 in the form of the above-described output data, and the product data of the valve 3 input to the gyro sensor unit 1 in a predetermined format (for example, data manually input to the unit 1 or read by a predetermined optical reader), together with the opening / closing count value of the valve 3 at that moment, and transmits the generated measurement data to a server 41 side.

[0201] The data acquisition means is a means that uses the measurement data described above as input and acquires second reference data substantially corresponding to the output data included in this measurement data from the second reference data table. Here, for the similarity of the output data to determine whether they are substantially the same (chart shape comparison method), a suitable known scheme such as area comparison is selected, and a means for achieving this goal is also implemented. Here, a specific method when the second reference data to be acquired is not present in a reference or when the pieces of the output data are not substantially the same is described below using Fig. 33 and Fig. 34 described.

[0202] The failure determination means is a means that refers to usage frequency data of the valve included in the second reference data acquired by the data acquisition means, and also refers to the opening / closing count value of the valve 3 included in the measurement data, and calculates a failure time of the valve 3, thereby determining failure prediction information of the valve 3 (further outputs the information to the terminal device for display).

[0203] For example, in Table 3, for a specific valve, the average usage frequency (times / month) and the open / close count until failure are recorded in advance, while the current open / close count of the valve is also obtained from the measurement data. From this, a period (month) from the current time until failure can be easily calculated. In this case, if the data indicates three months before the failure, an information message indicating three months before the ball seat replacement time can be sent via the Internet 43 to the PC 45 in a service center or to a terminal 44 carried by a service technician.Alternatively, reference data corresponding to the period of three months prior to the failure can be determined from the frequency of use of each of several valves available on the market, and when the measured angular velocity becomes approximately equal to these reference data, a notification can be given indicating three months prior to the failure.

[0204] As described below, since all reference data of the product is stored from a new product state until failure, notification of a replacement time can be made monthly incrementally, for example, three months in advance or two months in advance. When a notification calling for component replacement is issued but no maintenance is performed, for example, when the count value reaches fifty thousand times, an alarm may be triggered, indicating that a failure time has arrived. As described below, the control continues as failure prediction control until a leak of the used fluid exceeding an allowable value actually occurs and causes a system failure in which the piping system cannot be controlled, and ends after the output data at the time of failure is collected.

[0205] In a piping system where multiple valves are arranged on a single pipeline, this ball valve failure prediction control is nothing more than a rational replacement with regard to the maintenance of the entire system. That is, even if maintenance is only performed on a single valve, the operation of its piping system must be stopped, which causes enormous damage under the current circumstances. Therefore, a complete replacement is carried out even if another valve is arranged that is still usable. Since a valve with a lower usage frequency has a longer practical life expectancy than other valves of the same type and therefore does not need to be replaced until the next maintenance, according to this example, it is possible to simultaneously reduce the cost of replacing piping system components and shorten the overall maintenance time of the piping system.

[0206] Since the data is retained for the entire period from the time the product is new to the time the product fails, the usage status can be grasped even if the gyroscopic sensor is attached to a valve whose usage period has expired to a certain extent. In this way, failure prediction control can be quickly developed on the market. For example, if the sensor unit 1 is attached to a valve that has been used for half a year, reference data approximately corresponding to the measured angular velocity data is searched for, and a usage time is found from the corresponding operation count value and the average usage frequency. If the found usage time is half a year, this operation count value is recognized as correct, and failure prediction control can be started from an intermediate course.

[0207] Next, Fig. 33 and Fig. 34 an anomaly diagnosis process using the fourth anomaly diagnostic tool is described. Fig. Figure 33 is a flowchart illustrating the diagnosis process by the fourth abnormality diagnosis means. Process 48 is a process in which, when this abnormality diagnosis means is first executed, for the measurement data generated by the data generation means, it is determined whether a table matching the product data contained in this measurement data exists in the database 42. In the drawing, for each piece of product data, a reference presence flag is used to manage in advance whether a reference data table exists. Thus, using this flag, it is determined whether a table (the same product data) for search exists. If such a table exists, the process proceeds to process 49. If such a table does not exist, the process proceeds to process (A) in Fig.34 continued.

[0208] In Fig. 33, process 49 is a process in which the measurement data is input into the database 42. In process 50, the process is such that the data acquisition means receiving the measurement data input into the database 42 searches for and acquires a table record having the same opening / closing count as the opening / closing count contained in this measurement data, and then determines whether the output data (angular velocity diagram pattern) of this record (acquired data) is substantially equal to the output data contained in the measurement data. If it is determined that they are substantially equal, the process proceeds to process 52. If it is determined that they are not substantially equal, the process proceeds to process (B) in Fig.34. As a method for this comparison between two pieces of output data (a scheme for determining whether they are essentially the same), any of various well-known schemes (such as the concept of distance between data and similarity in sentence and form) may be appropriately selected.

[0209] At Process 52 In Fig.33, the process is designed to perform the prediction of the failure period based on the operation count by a failure determination means. Specifically, usage frequency data (count / period) and a failure opening / closing count (time) contained in the product data of the acquired data are acquired. On the other hand, a current opening / closing count (time) contained in the measured data is also acquired. From this, a measured failure prediction time point can be obtained from the measured data of the measured valve 3 as (failure opening / closing count - current opening / closing count) / usage frequency (period). In this way, a failure prediction time point can be specifically acquired using only simple processing, without the intervention of statistical processing (machine learning) with high processing costs.

[0210] It should be noted that in this process, the determination result may be acquired, for example, by referring to a determination result table not shown. This determination result table may, for example, be generated in advance for all the same product data in accordance with the opening / closing count value; for example, data records may be generated, each with a notification detail (e.g., normal, warning, or failure), a failure prediction time point (e.g., notification three months in advance or notification one month in advance), or the like as a column name in the order of magnitude with the opening / closing count value of the valve as the master key; and through appropriate means, by referring to a determination result table set with the same opening / closing count value as the opening / closing count value included in the measurement data, each piece of data, such as the notification detail and the failure prediction time point, may be acquired as a determination result.The notification details, or similar, can be divided into multiple predefined thresholds. This allows the failure prediction time to be captured by table reference without the intervention of calculation processing.

[0211] In process 52, a failure prediction time is determined. In process 53, notification details are collected. These can be transmitted to the terminal 44, 45 by suitable means so that they can be displayed. In the following process 54, it is determined whether a failure time has arrived. At this failure time, for example, it is determined whether the failure prediction time has occurred using a predetermined threshold as a boundary. If it is determined in this process 54 that the failure time has occurred, the process proceeds to process 55. If this is not the case, the process can return to process 49 to continue the anomaly diagnosis.

[0212] Process 55 is a warning process when it is determined that the failure time has arrived. The following process 56 determines whether a failure has occurred. If it is determined that no failure has occurred, the process can return to process 49 to continue anomaly diagnosis. Note that these processes 52 through 56 can basically be performed using the failure determination tool, but can of course be appropriately configured according to the implementation.

[0213] On the other hand, in Fig. 33, if no reference data table matching the product data exists, a second reference data table is regenerated upon this anomaly diagnosis. This process is the one described in Fig.34, and this process (A) is formed from processes 61 and 63. Since, as described later, whether the process proceeds to process (B), which is a process of changing the second reference data table, is controlled by a reference data change flag, a reference data change flag is first set at process 59.

[0214] That is, if the valves are the same but have a large difference in data in a 90-degree section from fully closed to fully open, and the tendency of a plurality of valves having a substantially similar degree of difference as those mentioned above persists, that is, if, for example, the reference data based on experiments conducted in-house is limited, and the data collection is significantly increased based on the number of products after sales on the market, it is considered that the data itself has deteriorated. It is also considered that product data that can identify the characteristics and specifications of the product, such as a special fluid used and an overly wide range of outdoor temperature and humidity, are inconsistent with the output data, even if these product data are available as reference data.It can also be assumed that product data for ball valves from another company does not exist at all, meaning that the reference data is not stored at all. To solve a variable deterioration factor in forecasting for failure prediction control, there are two processes (A) and (B) for reference data generation in this example. Process (A) is referred to as the reference data regeneration mode, and process (B) is referred to as the reference data modification mode. The entire data stored in . Fig. The process shown in Figure 34 is called a reference data generation process.

[0215] In Fig.34, process 60 is a process of re-storing second reference data generated from the measurement data into the database 42. For example, a case will be described where a test is performed on a product manufactured by the company itself before shipping the product. First, as measurement data, product data is input manually or automatically by a known optical reader sensor, and then, as output data, by rotation control of the ball valve by the actuator 2 attached to the valve 3, the approximately assumed average usage frequency of the valve, and angular velocity data for each angle each time from fully closed to fully opened from a time when the product is new and until a time when the product has failed are input through tests. This series of tests is performed N times, and the data is acquired as high-precision measurement data.In the following process 61, a second reference data table is populated. In the following process 63, a reference data present flag is set, indicating that the reference data is newly present, and the process ends and returns to the destination detail flow.

[0216] Next, in the process of regenerating second reference data, a case is described in which, for example, a ball valve of a product manufactured by another company is measured. This corresponds to the data set at the bottom of the second reference data table shown in Table 3. Accordingly, at the stage where the sensor unit 1 is attached and the product data is read out, it is recognized that the product is not the product manufactured by the company itself, but a product of another company. Therefore, in process (A), the series of measurements described above is not performed N times, but reference data is generated with one measurement (process 61), and the process returns to the Fig. 33 shown river.

[0217] On the other hand, if the product has the same product data and thus the existing second reference data table exists, but this reference data table does not contain any reference data that is approximately the same (process 51), this requires rewriting the second reference data itself, and a reference data change flag is set (process 59), and process (B) is executed. Since the existing reference data exists in this case, a step-by-step change process is performed.

[0218] In process (B), when acquiring output data from the measured data (process 64), a difference is detected between this output data and the existing second reference data, and the existing second reference data is incremented or decremented by 10% of this difference and set as the new second reference data. In processes 64 to 67, a counter ("counter" C) is set to 1, and angular velocity data is input as output data and subjected to similar processing ten times repeatedly. Then, the process exits the loop at process 65, and the reference presence flag is set at process 68, and then the process ends.

[0219] This ensures that a comparison with the measured data is performed at least ten times. This prevents the second reference data from being overwritten with measured data that is unique to a single ball valve. In particular, an abrupt change in the specifications of a ball seat of a ball valve manufactured by another company is less likely to be entered as product data. Therefore, in terms of accuracy, it is quite effective to compare and verify not only the product data but also the measured data, especially the angular velocity data.

[0220] In addition, with regard to rewriting reference data, there is also a weighting method such as weighted averaging (weighting with the degree of difference at a characteristic part) as another means. This is as follows. For example, if a valve manufactured by another company is a target, if an abrupt change in the specifications of a ball seat causes switching to another ball seat for some technical reason, since each ball seat has its own angular velocity, the same valves will have a large fluctuation range with respect to the existing reference data in most of the opening / closing range from fully closed to fully open.If a similar trend occurs continuously across a large number of valves, the product data is weighted and the reference data is gradually written with a fluctuation ratio smaller than the fluctuation range (for example, if the output data fluctuates by 10% compared to the output data in the previous reference data, rewriting is performed gradually with a fluctuation ratio of 2%). This enables the generation of reference data from the output data (measurement data) even if the reference data is not saved in advance, facilitating the implementation of a failure prediction system and improving forecast accuracy.

[0221] In this way, in the generation of reference data, by combining a process of regenerating and establishing reference data and a process of rewriting the existing reference data as leveled and weighted, reference data of the product can be generated before the product is shipped, reference data can be automatically generated by inputting measurement data of a product manufactured by another company on the market, and various situations such as an abrupt change in the use of a component on the market can be addressed.

[0222] With the above-described gyroscopic sensor unit 1 used for the valve 3 in use, it is therefore possible to perform a process for generating second reference data including the output data and the product data in accordance with the opening / closing count value of the valve 3 from the measurement data acquired by the measurement of this gyroscopic sensor unit 1. This second reference data generation process includes, as shown in Fig. 34, a reference data regeneration mode and a reference data modification mode.

[0223] Furthermore, in a piping system in which a plurality of valves are arranged to be maintained at predetermined intervals, it is possible to perform a piping system maintenance method in which, for each of the plurality of valves, a prediction of a failure time is performed for each individual valve by using the valve condition detecting system of the present invention to detect each prediction result, and in which a valve for which this prediction result exceeds the interval is excluded from a maintenance target.

[0224] It should be noted that Fig. 35 and Fig. 36 are diagrams obtained by measuring a rotational movement by an acceleration sensor instead of the gyro sensor 7 under the same conditions as in the other examples ( Fig. 25 to Fig.30). This acceleration sensor, although not shown, is mounted at a location on the back of the fitting 5 of the monitoring unit 1 to measure the acceleration in three XYZ axes in Fig. 1. The measurement is taken at a position with approximately the same amount of movement as that of the gyro sensor 7 built into the monitoring unit 1.

[0225] In Fig. 35, the acceleration is measured under similar conditions as in Fig. 25 to Fig. 27 measured. Fig. 35(a) is a graph of acceleration in the X-axis direction, Fig. 35(b) is a graph of acceleration in the Y-axis direction and Fig. Figure 35(c) is a graph of the acceleration data in the Z-axis direction. The same applies to Fig. 36, and the acceleration is under similar conditions as in Fig. 28 to Fig. 30 measured. Fig.36(a) is a graph of acceleration in the X-axis direction, Fig. 36(b) is a graph of acceleration in the Y-axis direction and Fig. Figure 36(c) is a graph of acceleration data in the Z-axis direction. While the graph's markers are similar to those in the other drawings, the right vertical axis in Fig. 35 and Fig. 36 represents the acceleration, and both have extremely small increments (0.005 G to 0.02 G, G is the acceleration due to gravity).

[0226] As from Fig. 35 and Fig.As can be seen from Figure 36, for the acceleration in any of the three axis directions, only a pattern fluctuating randomly within an extremely small range can be detected. Although a pattern resembling protruding peaks is partially measured, it is merely a pattern that appears only after setting an extremely small acceleration scale, and it cannot be said that the measurement is not at a level where a graph pattern can be detected with a practical accuracy for valve diagnosis. Thus, it has been confirmed that although the acceleration sensor is an inertial sensor of the same type as the gyroscopic sensor, the rotational movement of the valve cannot be detected with the required accuracy by only the acceleration sensor.

[0227] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to the description of the above embodiments and can be modified in various ways within a range that does not deviate from the gist of the invention described in the claim of the present invention. List of reference symbols 1 monitoring unit (sensor unit or unit) 2 actuator 3 Ball valve (rotary valve) 4 Control shaft (rotating shaft, valve spindle) 7 Gyro sensor 14 Output shaft (rotating shaft, valve spindle) 15 spindle (rotary shaft, valve spindle) 26a, 27a, 30a flow path 30 ball (valve body) 41 servers, cloud servers 42 Database A1, A2 ball seat (valve seat) (wear part) B Spindle bearing (wear part) C Stuffing box packing (wear part) T1, T2, T3 characteristic value

Claims

[1] A method for detecting a valve condition for performing condition monitoring of a valve (3) based on angular velocity data of a valve spindle (4) that opens and closes the valve (3), wherein a sensor unit (1) having at least one semiconductor-type gyro sensor (7) is attachably and detachably mounted on the valve spindle (4), and the angular velocity data includes an angular velocity detected by the sensor unit (1) in accordance with a rotational movement of a valve body (30) of the valve (3) from fully open to fully closed or from fully closed to fully open, the valve (3) being a rotary valve that opens and closes or controls a flow path (26a, 27a, 30a) by rotating the valve spindle (4), the angular velocity data indicating a measured value of the angular velocity in one direction,which is substantially equal to the direction of rotation of the valve spindle (4), the rotary valve is a quarter-turn ball valve or a butterfly valve, and wherein the valve (3) has a structure with a ball of the ball valve and a ball seat (A1, A2) serving as a valve seat or a structure with a valve body of the butterfly valve and a seat serving as a valve seat, wherein the condition monitoring further comprises: Calculating a rotation angle of the valve spindle (4) based on the angular velocity data to calculate opening degrees of the valve body (30), and Calculating transitions of the opening degrees and the angular velocity of the valve body (30) according to a time elapsed from fully open to fully closed or from fully closed to fully closed, and based on a number of openings and closings of the valve body (30), wherein a numerical value representing characteristics of the changes in the angular velocity in at least one of a time region T1 of an initial opening degree of the valve body (30), a time region T2 of an intermediate opening degree of the valve body (30) and a time region T3 of a final opening degree of the valve body (30) is stored in advance as a reference measurement value based on the opening degrees of the valve body (30), and wherein the wear state of the ball seat (A1, A2) or the seat is detected by comparing a numerical value obtained from the angular velocity actually measured by the gyro sensor (7) in at least the time region T1 of the initial opening degree of the valve body (30), the time region T2 of the intermediate opening degree of the valve body (30) and / or the time region T3 of the final opening degree of the valve body (30) with the numerical value stored as a reference value corresponding to the number of openings and closings of the valve body (30) during the actual measurement of the angular velocity. [2] A system for detecting a valve state comprising a valve (3), a sensor unit (1) having at least one semiconductor-type gyro sensor (7), and a server (41) connected to the sensor unit (1) and comprising a database (42), wherein the gyro sensor (7) measures angular velocity data of a valve spindle (4) that opens or closes the valve (3), and the system for detecting the valve state is configured to detect a state of the valve (3) based on the angular velocity data comprising a measured angular velocity, wherein the sensor unit (1) is attachably and detachably mounted on the valve spindle (4) of the valve (3), and the gyro sensor (7) measures angular velocity readings in the same direction as a direction of rotation of the valve spindle (4), wherein the valve (3) is a quarter-turn ball valve comprising a ball which is a spherical body (30) as a rotary valve for opening and closing or controlling a flow path (26a, 27a, 30a) by rotating the valve spindle (4), and a ball seat (A1, A2) which is a valve seat, or a butterfly valve with a valve body (30) as a rotary valve and a seat as a valve seat, wherein the angular velocity measurement values ​​comprise an angular velocity of the valve spindle (4) as a function of a rotational movement of the valve body (30) of the valve from fully open to fully closed or from fully closed to fully open, detected by the sensor unit (1), wherein the sensor unit (1) or the server (41) further calculates a rotation angle of the valve spindle (4) from the angular velocity measurement values ​​to calculate opening degrees of the valve body (30), and transitions of the opening degrees and the angular velocity of the valve body (30) according to a time elapsed from fully open to fully closed or from fully closed to fully open, and based on a number of openings and closings of the valve body (30), wherein the database (42) stores in advance as reference data a numerical value representing the characteristics of the changes in the angular velocity in at least a time region T1 of an initial opening degree of the valve body (30), a time region T2 of an intermediate opening degree of the valve body (30) and / or a time region T3 of a final opening degree of the valve body (30) based on the opening degrees of the valve body (30), and wherein the sensor unit (1) or the server (41) detects the wear state of the ball seat (A1, A2) or the seat by comparing a numerical value obtained from the angular velocity actually measured by the gyro sensor (7) in at least the time region T1 of the initial opening degree of the valve body (30), the time region T2 of the intermediate opening degree of the valve body (30) and / or the time region T3 of the final opening degree of the valve body (30) with the numerical value stored as a reference value corresponding to the number of openings and closings of the valve body (30) during the actual measurement of the angular velocity.

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