Symptom detection system, symptom detection method, and symptom detection program

Abstract

Improve the ability to detect abnormalities in production equipment, or identify the cause of abnormalities and respond quickly. [Solution] The symptom detection system includes one or more computers that perform the following operations: acquire sensing data representing at least one of light, sound, temperature, vibration, odor, and the generation of specific substances in a non-contact manner from production equipment including an inspection target; calculate the degree of abnormality for the acquired sensing data; detect signs of abnormality and their causes based on causal relationship information representing the correspondence between the cause of the abnormality and the characteristics of the degree of abnormality, and the degree of abnormality corresponding to the acquired sensing data; and output information indicating the detected signs of abnormality and their causes.

Description

[Technical Field]

[0001] The present disclosure relates to a sign detection system, a sign detection method, and a sign detection program. [Background technology]

[0002] Conventionally, an anomaly detection device has been proposed that includes a data acquisition unit that reads image data and process data from a storage device that stores image data continuously output by an imaging device equipped in the production equipment and process data continuously output by a sensor attached to the production equipment; an anomaly determination unit that calculates a degree of image abnormality using a model that has learned the tendency of the feature values ​​of image data in a normal state and the feature values ​​of the image data acquired by the data acquisition unit, and calculates a sensor value abnormality degree that represents the degree of modulation of the process data read by the data acquisition unit; and a cause diagnosis unit that outputs a cause corresponding to the sensor value abnormality degree and image abnormality degree calculated by the anomaly determination unit, based on causal relationship information that defines the correspondence between the cause and the combination of the sensor value abnormality degree and image abnormality degree (Patent Document 1).

[0003] In addition, an equipment status monitoring system has been proposed that has a sensor that outputs data indicating the status of the equipment used for status monitoring as sensor data, a communication unit that transmits the sensor data, and a power supply unit that supplies power to the sensor and communication unit, and that has a sensor node common to multiple pieces of equipment (Patent Document 2).

[0004] A remotely installed equipment status diagnostic device has also been proposed (Patent Document 3). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2023 / 127748 [Patent Document 2] Japanese Patent Publication No. 2023-7350 [Patent Document 3] Japanese Patent Application Publication No. 07-49713 Summary of the Invention [Problem to be solved by the invention]

[0006] In general, it is desirable to predict anomalies in production facilities and minimize their impact on safety, stability, product quality, costs, the environment, and so on. However, when changes appear in process data in a plant, for example, it is not uncommon for a problem to have progressed to some extent. Therefore, this technology aims to at least one of improving the ability to detect anomalies in production facilities and identifying the cause of the anomaly and taking early action. [Means for solving the problem]

[0007] The sign detection device according to the present disclosure is, for example, as follows. (Aspect 1) Acquiring sensing data representing at least one of light, sound, temperature, vibration, odor, and generation of a specific substance in a non-contact manner from production equipment including an inspection target; Calculating an abnormality level for the acquired sensing data; Detecting a sign of the abnormality and its cause based on causal relationship information indicating a correspondence relationship between the cause of the abnormality and characteristics of the abnormality level, and the abnormality level corresponding to the acquired sensing data; outputting information indicating the detected sign of the abnormality and its cause; A predictive detection system including one or more computers that execute the above. (Aspect 2) In aspect 1, There are two or more types of the acquired sensing data, and the calculation of the degree of abnormality is performed on the two or more types of the sensing data, The causal relationship information may represent a correspondence relationship between the cause of the abnormality and characteristics of the degree of abnormality based on each of the two or more types of sensing data. (Aspect 3) In aspect 1 or 2, the production facility is a chemical plant, The sensing data may be data different from process data measured on a processing object of the chemical plant. (Aspect 4) In aspect 3, The one or more computers further acquiring the process data; Calculating an abnormality degree for the process data; The signs of the abnormality and their causes may be detected based on a combination of the degree of abnormality of two or more types of sensing data of the inspection target of the chemical plant and the degree of abnormality of the process data. (Aspect 5) In any one of aspects 1 to 4, There are multiple inspection targets in the production equipment, Further, the system includes a moving means that is equipped with a sensor for acquiring the sensing data and that can move within the area of ​​the production facility according to a preset program; The sensor may acquire the sensing data from each of the plurality of inspection targets. (Aspect 6) In aspect 5, The moving means may include a holding mechanism that holds the sensor so that its orientation or position can be changed. (Aspect 7) In aspect 5 or 6, An identification sign indicating that the production equipment is subject to inspection is provided on or around the production equipment, The moving means may start acquiring the sensing data by detecting the identification mark by image recognition. (Aspect 8) In any one of aspects 5 to 7, When the one or more computers are unable to acquire the sensing data from the sensor, the one or more computers may request the means of transportation to recreate the sensing data, or may notify a user terminal. (Aspect 9) In any one of aspects 1 to 8, The one or more computers may acquire two or more types of the sensing data, and detect multiple combinations of signs of the abnormality and their causes based on the degree of abnormality corresponding to each of the acquired sensing data. (Aspect 10) In aspect 9, The one or more computers may output combinations of multiple detected signs of anomaly and their causes in order of likelihood based on the degree of agreement between the characteristics of the degree of anomaly represented by the causal relationship information and the degree of anomaly corresponding to each of the acquired sensing data. (Aspect 11) In aspect 9 or 10, The one or more computers may store information representing the repair history of the production equipment, and for a combination of multiple detected signs of abnormality and their causes, it may be determined that the more recently a part was repaired, the less likely the defect is caused by that part. (Aspect 12) Acquiring sensing data including at least one of light, sound, temperature, vibration, odor, and generation of a specific substance in a non-contact state from production equipment including an inspection target; Calculating an abnormality level for the acquired sensing data; Detecting a sign of the abnormality and its cause based on causal relationship information indicating a correspondence relationship between the cause of the abnormality and characteristics of the abnormality level, and the abnormality level corresponding to the acquired sensing data; outputting information indicating the detected sign of the abnormality and its cause; The predictive detection method is executed by one or more computers. (Aspect 13) Acquiring sensing data representing at least one of light, sound, temperature, vibration, odor, and generation of a specific substance in a non-contact manner from production equipment including an inspection target; Calculating an abnormality level for the acquired sensing data; Detecting a sign of the abnormality and its cause based on causal relationship information indicating a correspondence relationship between the cause of the abnormality and characteristics of the abnormality level, and the abnormality level corresponding to the acquired sensing data; outputting information indicating the detected sign of the abnormality and its cause; A predictive detection program for executing the above on one or more computers. (Aspect 14) Sensing data acquisition work to acquire sensing data representing at least one of light, sound, temperature, vibration, odor, and the generation of specific substances from production equipment including the inspection target in a non-contact state. The degree, a calculation step of calculating an abnormality degree for the acquired sensing data; a detection step of detecting a sign of the abnormality and its cause based on causal relationship information indicating a correspondence relationship between the cause of the abnormality and a plurality of characteristics of the abnormality level and the abnormality level corresponding to the acquired sensing data; an output step of outputting information indicating the detected sign of abnormality and its cause; a process data acquisition step of further acquiring process data measured on the processing object of the production facility, the process data being different from the sensing data; a second calculation step of calculating an abnormality degree for the process data; Including, The symptom detection method, wherein the output step is performed while the number of items of the process data indicating an abnormality is smaller than the number of items of the sensing data indicating an abnormality.

[0008] The contents of the means for solving the problem can be combined as much as possible without departing from the problem and technical idea of ​​the present disclosure. The contents of the means for solving the problem can be provided as a device such as a computer or a system including multiple devices, a method executed by a computer, or a program executed by a computer. A recording medium storing the program may also be provided. [Effects of the Invention]

[0009] According to the disclosed technology, it is possible to improve the performance of detecting abnormalities in production equipment, or to identify the cause of the abnormality and take action early. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of a system according to this embodiment. [Figure 2] FIG. 2 is a diagram for explaining acquisition of sensing data. [Figure 3] FIG. 3 is a schematic perspective view showing an example of a holding structure for the five senses sensor. [Figure 4] FIG. 4 is a diagram showing an example of the flow of data and processing in the entire system. [Figure 5] FIG. 5 is a process flow diagram showing an example of a sign detection process executed by the system. [Figure 6] FIG. 6 is a diagram for explaining anomaly detection using an autoencoder. [Figure 7] FIG. 7 is a diagram illustrating an example of the determination logic. [Figure 8] FIG. 8 is a diagram illustrating another example of the determination logic. [Figure 9] FIG. 9 is a diagram illustrating another example of the determination logic. [Figure 10] FIG. 10 is a diagram for explaining another example of the determination logic. [Figure 11] FIG. 11 is a diagram for explaining another example of the determination logic. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of the symptom detection device will be described with reference to the drawings.

[0012] <Embodiment> FIG. 1 is a diagram showing an example of a system according to this embodiment. The system 100 includes a plant 1, a control station 2, a warning detection device 3, and a user terminal 4. These components are communicatively connected via a predetermined control network. The system 100 is, for example, a distributed control system (DCS) and includes multiple control stations 2. That is, the control system of the plant 1 is divided into multiple sections, and each control section is controlled in a distributed manner by the control station 2.

[0013] The plant 1 is, for example, a process plant. A process plant is a plant that mainly handles materials with irregular shapes, such as fluids, gases, and powders, and manufactures products using processes such as reactions and synthesis. Process plants include, but are not limited to, plants that handle technologies such as chemical products, petroleum, metals, paper and pulp, pharmaceuticals, glass and cement, and steel. Preferably, the plant 1 is, for example, a chemical process plant, a production facility that achieves some purpose through a series of chemical processes. The plant 1 is equipped with sensors (process instrumentation 111, five senses sensor 124), and plant data obtained from signals output from the sensors is transmitted to the predictive detection device 3 via the control station 2. The system 100 is compliant with a communication standard such as OPC (OLE for Process Control), for example. In addition, the plant data is information for monitoring the operating status of the plant 1, and this plant data includes process data and sensing data.

[0014] Process data is data representing the state of a processing object being processed by, for example, the production equipment 11 provided in the plant 1. Processing objects may include raw materials, intermediates, products, catalysts, utilities (heat transfer medium, etc.), waste liquids, wastewater, etc. The production equipment 11 may also include reactors, distillation columns, heat exchangers, pumps, compressors, tanks, piping, etc. In particular, process data may be signals or data representing the state of a processing object obtained directly or indirectly by process instrumentation 111, such as sensors or field devices, attached to the production equipment 11, etc., to detect the state of the processing object or to understand the operating state (operating parameters) of the production equipment 11 with respect to the processing object. For example, the process data may be direct signals or data from the process instrumentation 111 attached to the production equipment 11, or data obtained by processing the acquired signals or data in advance. The process data may also include information from temperature sensors, pressure sensors, flow meters, level meters, pH measuring instruments, concentration meters, density meters, viscometers, etc. In this embodiment, the process data is data obtained without going through the patrol device 12 described later, and is data transmitted from the plant 1 to the symptom detection device 3 without going through the patrol device 12.

[0015] On the other hand, the sensing data is data obtained by the patrol device 12 (described later) and transmitted from the plant 1 to the sign detection device 3 via the patrol device 12 (described later). For example, the sensing data represents the status of each piece of production equipment to be inspected. The sensing data is generated based on information sensed by at least one of the five senses (human senses) for the production equipment 11 to be inspected in the plant 1. Furthermore, the sensing data is also information that has not yet been digitized and is used to grasp the status of the production equipment to be inspected. In other words, the sensing data is a signal or data obtained by contactlessly acquiring information from the inspection target via a five-sense sensor 124 (substituting for human senses), outputting it in digital form (sampled and quantized), or it refers to data transmitted and received via a contactless method, such as wireless communication, to acquire signals or data from the inspection target. Furthermore, unlike the process data acquisition method described above, the sensing data can be acquired contactlessly via wireless communication or by accessing the five-sense sensor 124 from a remote location relative to the inspection target. The objects of the sensing include light, sound, temperature, vibration, odor, and the occurrence or presence of specific substances at the inspection target. Specifically, the sensing data is at least one of visible light, infrared light, and ultraviolet light obtained from the inspection target, sound emitted from the inspection target, the temperature and vibration of the inspection target itself, odor around the inspection target, and the occurrence or presence of specific substances. The data is acquired by measuring these without physically contacting the inspection target with the five senses sensor 124. The orientation and position of the five senses sensor 124 may be changed for each inspection target so that measurements can be taken in close proximity to the inspection target even without contact. The sensing data may be the signal itself output by a sensor or may be data obtained by processing the signal. The five senses sensor may include, for example, at least one of an imaging device (image sensor) that acquires visual information (light) such as an image or video, a microphone (acoustic sensor) that acquires auditory information such as sound, an odor sensor that acquires olfactory information by converting the detection result of an odorant (the occurrence of a specific substance) into an electrical signal, and a tactile sensor that acquires tactile information.Tactile sensors include inertial sensors (acceleration sensors, angular velocity sensors, IMU (Inertial Measurement Unit)), force sensors, etc. The sensors may be slip sensors, vibration sensors, temperature sensors, thermal cameras, etc. In other words, the sensing data is data obtained through various five-sense sensors that are primarily in a state of no physical or electrical contact with the inspection target. Therefore, the five-sense sensors 124 that obtain the sensing data are positioned at a physical and spatial distance from the equipment to be inspected, and monitor the status of the production equipment 11.

[0016] Alternatively, when various five-sense sensors are installed in the production equipment 11, the sensing data may be data transmitted and received by the sign detection device 3 via the patrol device 12 (described later) in a non-contact manner, such as wireless communication, when the signals and data from the sensors are acquired by the sign detection device 3. In this case, the various five-sense sensors 124 that acquire the sensing data may be installed in the production equipment 11, and the data and signals emitted therefrom may be transmitted from a physical and spatial distance away from the production equipment 11.

[0017] In particular, in the present embodiment, the sensing data may be acquired by acquiring five-sensory data and signals as the five-sensory sensors 124 themselves approach each piece of production equipment 11 (inspection target) fixed within the plant 1. Alternatively, data and signals from the five-sensory sensors 124 attached to each piece of production equipment 11 may be acquired by a mobile receiving device when the sensor approaches the production equipment 11. In such cases, the five-sensory sensors 124 themselves or the receiving device themselves are mobile, and, for example, the five-sensory sensors 124 and receiving device are fixed to the patrol device 12 described below. Data acquired in this manner can also be considered sensing data in the present embodiment. In this case, the patrol device 12 patrols a path independent of the path of the processing object described above. The processing object and production equipment 11 are then monitored externally to collect sensing data. In this way, the influence of the processing object during measurement can be eliminated, and measurement flexibility can be increased, such as by stopping or reversing the patrol device 12 for remeasurement.

[0018] The plant 1 in FIG. 1 includes production equipment 11, a patrol device 12, and an access point (AP) 13. The production equipment 11 includes tanks, reactors, piping, and the like for producing chemicals, and corresponds to the inspection target in this embodiment. The production equipment 11 is assumed to be equipped with general process instrumentation 111 for measuring process data. The patrol device 12 is an example of a vehicle equipped with a processor 121, a storage device 122, a communication interface (IF) 123, a five-sense sensor 124, and a drive device 125. The patrol device 12 will be described in detail later; the five-sense sensor 124 acquires the sensing data described above. The AP 13 is a relay device that connects a computer to a network within the system and enables communication. In this embodiment, the patrol device 12 is wirelessly connected to the AP 13 via the communication IF 123, and can communicate with the sign detection device 3.

[0019] Each sensor signal from the process instrumentation 111 fixed to the production equipment 11 of the plant is converted into process data and transmitted to the control station 2. The control station 2 receives the process data from the plant 1 and outputs control signals to the plant 1. Actuators such as valves and other equipment provided in the plant 1 are controlled based on the control signals. The control station 2 may also output the process data obtained from the plant 1 to the symptom detection device 3 or the user terminal 4. The control station 2 may also function as an annunciator that outputs an alarm or the like to the user terminal 4 when, for example, the symptom detection device 3 detects an abnormality or its precursor. At this time, information regarding an operation to resolve the abnormality or its precursor may be output to the user terminal 4 depending on the cause of the detected abnormality or its precursor.

[0020] The early warning device 3 directly acquires sensing data output by the plant 1 and also acquires process data output by the plant 1 via the control station 2. In this embodiment, the early warning device 3 primarily uses sensing data among plant data to detect abnormalities or their precursors in the plant 1 and identify their causes. The early warning device 3 is equipped with an early warning model based on a knowledge base that stores correspondences between expected causes and, for example, effects that manifest as abnormalities. The early warning model is a model for identifying abnormality precursors and their causes by detecting deviations from normal states in changes in sensing data. The early warning device 3 may extract, for example, actions to suppress the occurrence of an abnormality based on the identified causes and a table that stores information indicating the causes of the abnormality or its precursors and actions to take to address them, and present the extracted actions to the user. Note that a deviation from the expected (desired) operating state or normal operating state in the plant 1 is also referred to as "modulation."

[0021] Below, we will explain a system that uses sensing data to detect signs of abnormality and identify their causes. Figure 1 shows a block diagram showing an example of the configuration of a sign detection device 3. The sign detection device 3 is a computer, and includes a processor 31, a storage device 32, and a communication interface (I / F) 33. The processor 31 may be, for example, a CPU (Central Processing Unit), an MCU (Micro Controller Unit), an MPU (Micro Processing Unit), ), DSP (Digital Signal Processor), FPGA (Field Programmable Gate Array) ), ASIC (Application Specific IC), ASSP (Application Specific Standard The processor 31 performs the processes described in this embodiment by executing a program, for example. The storage device 32 includes a RAM (Random Access Memory). The main storage device is a main storage device such as a hard disk drive (HDD), a solid state drive (SSD), a flash memory, etc. The main storage device temporarily stores the program read by the processor 31 and secures a working area for the processor 31. The auxiliary storage device stores the program executed by the processor 31 and other sensing data. The communication IF 33 is It is a network module for connecting to the control network of the system 100 and performs communication based on a specified protocol.

[0022] The user terminal 4 is a computer installed in an operation room and used by users such as operators (board operators) who operate the plant 1. The user terminal 4 also includes a processor, a storage device, and a communication IF. These components are similar to those of the processor 31, storage device 32, and communication IF 33 of the early warning device 3. The user terminal 4 also includes a user interface (UI) such as a display panel, a touch panel stacked on the display panel, a keyboard, and a pointing device, and outputs information to the user and receives operations from the user via the UI. The user terminal 4 may display sensing data acquired from the control station 2. If the early warning device 3 detects an abnormality or its precursor, information to that effect is notified to the user terminal 4, and the user terminal 4 outputs a warning or the like as appropriate. The user terminal 4 also transmits a control signal to the control station 2 to control the operation of the plant 1 based on the user's operation.

[0023] FIG. 2 is a diagram for explaining the acquisition of sensing data. The plant 1 shown in FIG. 2 includes a production device 11 (corresponding to an inspection target), a patrol device 12 equipped with five-sense sensors, a patrol route 14, and an identification display 15. The processor 121 of the patrol device 12 is a calculation processing device such as a CPU, and performs each process described in this embodiment by executing a program. The processor 121 also controls the drive device 125 to move the patrol device 12, and at a predetermined position, stores sensing data converted based on signals acquired by the five-sense sensors 124 in the storage device 122. The storage device 122 includes, for example, a main storage device and an auxiliary storage device. The communication IF 123 is a communication module for transmitting and receiving data. The communication IF 123 transmits the sensing data to the sign detection device 3, for example, via an AP 13 installed at a predetermined position within the plant 1.

[0024] As described above, the sensing data is data representing the state of the production equipment 11, obtained using various sensors that are not in physical contact with the production equipment 11 (i.e., spatially separated). Such sensing data can be measured by the five-sense sensor 124 mounted on the patrol device 12. Alternatively, the sensing data may further include data received by a receiving device that is not in physical or spatial contact with a sensor arranged in contact with the production equipment 11 (the sensor and the receiving device communicate signals wirelessly). Such sensing data can be collected by a receiving device mounted on the patrol device 12 or a receiving device installed within the plant 1. The processor 121, storage device 122, communication IF 123, five-sense sensor 124, and drive device 125 are fixed to the chassis (main body) of the patrol device 12. This avoids the effects of movement of the patrol device 12 and external vibrations, eliminating noise during data collection. In particular, the fact that the five senses sensor 124 is fixed to the chassis of the patrol device 12 prevents the five senses sensor 124 from shaking due to the influence of wind outdoors, and does not reduce the accuracy of measurement.

[0025] Furthermore, the five senses sensor 124 may be attached to the chassis of the patrol device 12 in an adjustable manner in any direction or position. For example, if the five senses sensor 124 is a camera, the direction of the lens, or if it is a microphone, the sound collection part, can be directed toward the production equipment 11 to be inspected while moving the patrol device 12, and the height position can be adjusted, so accurate data can be obtained without being obstructed by the shadow of the equipment. This allows for a more accurate understanding of abnormalities in the production equipment 11. Furthermore, since only one sensor such as a camera or microphone is needed and its direction can be changed for measurement, the patrol device 12 can be made lighter. Furthermore, since multiple production equipment 11 in the plant 1 can be measured with a minimum number of sensors, there is no need to attach multiple sensors to the plant 1 or the patrol device 12, resulting in a simple system. Each sensor mounted on the patrol device 12 The orientation of the sensor can be changed or adjusted by a pre-programmed program, or it may be adjustable by remote control (manually).

[0026] The five senses sensor 124 is a variety of sensors corresponding to items detected by the five human senses, such as an imaging device, an infrared sensor, a microphone, an odor sensor, a gas detector, and a tactile sensor, and includes at least one of these. In the example of FIG. 2 , the five senses sensor is provided on the mobile patrol device 12, but it may also be fixed within the plant 1. Depending on the type of sensor, some may be mounted on the patrol device 12 and others installed within the plant 1. For example, a tactile sensor such as an acceleration sensor for detecting vibrations may be fixed to the production equipment 11 to be inspected, and an imaging device or microphone may be mounted on the patrol device 12. Alternatively, the patrol device 12 may be stopped for each production equipment 11 to be inspected, and a sensor attached to the tip of an arm that extends from the patrol device 12 may be brought into contact with the production equipment 11 to perform measurements. Furthermore, when the five senses sensor 124 is fixed to the production equipment 11, sensing data may be transmitted to the patrol device 12 or the AP 13 via wireless communication.

[0027] The patrol device 12 may hold the five-sensory sensor 124 so that at least one of its orientation and position can be changed. FIG. 3 is a schematic perspective view showing an example of a holding structure for the five-sensory sensor 124. The five-sensory sensor 124 in FIG. 3 is an imaging device (camera) that acquires image data, and is suspended by a holder 127 from a rotating platform 126 rotatably mounted on the lower part of the chassis of the patrol device 12, for example. The five-sensory sensor 124 can be rotated around a vertical axis of rotation by, for example, rotating the rotating platform 126 using a motor. In this way, the five-sensory sensor 124 can change its orientation (pan angle) horizontally, for example, over 360 degrees, and various production equipment 11 present around the patrol route 14 can be used as measurement targets. The holder 127 may further include a motor 128 for rotating the orientation (tilt angle) of the five-sensory sensor 124 up and down around the horizontal axis of rotation. In this case, it is preferable that the depression angle of the five-sensory sensor 124 is at least 90 degrees (directly above) and the elevation angle is at least 90 degrees (directly below). Furthermore, if the five-sensory sensor 124 is an imaging device, a so-called fisheye lens may be used to capture a wide range of images, or images captured using two or more lenses may be stitched together. Furthermore, the holding unit 127 extending vertically from the chassis may be equipped with a linear actuator that expands and contracts in the direction of extension. This allows the five-sensory sensor 124 to be brought closer to the production equipment 11 to be inspected or to measure from an angle looking up from below. By holding various five-sensory sensors 124 in a manner that allows at least one of the orientation and height position to be changed, it becomes possible to measure production equipment 11 located in various directions around the patrol route 14. Furthermore, a composite sensor equipped with multiple types of sensors may share the rotating table 126, holding unit 127, and motor 128.

[0028] The drive device 125 is, for example, a driving motor that controls the rotation of the drive wheels, and causes the patrol device 12 to travel or stop based on the control of the processor 121. The drive device 125 may also include a steering motor for changing the steering angle of the wheels. Note that when the five senses sensor 124 is fixed to the production equipment 11, the five senses sensor 124 does not have the drive device 125.

[0029] The patrol device 12 may have an explosion-proof housing for housing at least some of the above-mentioned components. Alternatively, the explosion-proof housing may include all of the processor 121, storage device 122, communication IF, five senses sensor 124, and drive device 125. In this way, even if there is a possibility of a flammable substance leaking from the plant 1, the equipment and circuits included in the patrol device 12 can be prevented from being affected. The structure of the housing of the patrol device 12 can be changed as appropriate depending on the type of production equipment 11 included in the plant 1, for example.

[0030] The patrol path 14 may be, for example, a rail laid within the plant 1, and the patrol device 12 travels along the patrol path 14. The patrol path 14 is established in advance within the plant 1. In the example of FIG. 2, the patrol path 14 is an erected rail, and the patrol device 12 is a suspended running body, but this is not limited to this example. For example, the patrol path 14 may be a line drawn on the floor of the plant 1, and the patrol device 12 may be equipped with a sensor for tracing the line, or may be a robot that moves autonomously within the facility while sensing surrounding obstacles. Alternatively, the patrol path 14 may be a predetermined flight path, and the patrol device 12 may be a drone such as a multicopter that flies autonomously. Furthermore, the patrol path 14 may be a unit including a processor, a memory device, communication equipment, and five senses sensors so that it can be worn by a person and used to patrol the plant 1.

[0031] The identification mark 15 is, for example, identification information displayed on the production equipment 11 to be inspected or in its vicinity, and may be displayed on a sign or the like. The identification information is information for identifying the production equipment to be inspected. The identification information may be a predetermined mark, symbol, two-dimensional code, barcode, or the like. The processor 121 of the patrol device 12 detects the presence of the inspection target by image-recognizing the identification information using an imaging device (five-senses sensor 124), and performs measurement using the five-senses sensor 124, for example, by stopping the patrol device 12, or while the patrol device 12 is traveling, or by slowing down the speed. For example, the patrol device 12 may be configured to detect the identification mark 15 only at the desired measurement point by adjusting the accuracy of the image recognition or the placement or orientation of the identification mark 15.

[0032] The patrol device 12 may determine that it has reached the desired measurement point based on location information or the like, rather than using the identification display 15. Alternatively, an image of the desired measurement point may be machine-learned in advance using an image acquired by an imaging device while traveling along the patrol route 14, so that the processor 121 can recognize the measurement point based on the image acquired from the imaging device while the patrol device 12 is traveling. The processor 121 may also recognize the measurement point based on the three-dimensional shape of the surrounding area acquired by a LiDAR or other device, not limited to an imaging device, based on the three-dimensional shape of the production equipment 11 to be inspected, the distance to the production equipment 11, the elapsed traveling time, and the like. The processor 121 may also recognize the measurement point by receiving a wireless signal (command) transmitted from a transmitter installed near the production equipment 11 using a receiver mounted on the patrol device 12. The wireless signal may be transmitted by any method, including a beacon signal or short-range wireless communication. Travel of the patrol device 12 and measurement by the five-sense sensor 124 may be performed manually remotely while viewing information acquired from the imaging device. Two or more of the above methods for detecting measurement points can also be implemented in combination.

[0033] FIG. 4 illustrates an example of the data and processing flow in the entire system. FIG. 4 illustrates the patrol device 12, process data 21 stored in the storage device of the control station 2, the sign detection device 3, the user terminal 4, and the user 5. The processor 31 of the sign detection device 3 executes a program according to the embodiment to function as an acquisition unit 311, an anomaly level calculation unit 312, a sign detection unit 313, a cause diagnosis unit 314, and an output control unit 315. The storage device 32 of the sign detection device 3 also stores causal relationship information 321. The causal relationship information 321 represents the correspondence between the cause of an anomaly and the characteristics of the anomaly level, and will be described in detail later. The example in FIG. 4 illustrates a case in which process data is used in addition to data obtained from the five senses sensors 124 to comprehensively identify signs of anomalies and their causes in production equipment. The sign detection device 3 is located on a network constituting the system, facilitating the exchange of data from the five senses sensors 124 and process data.

[0034] The processing of signals from the five senses sensor 124 will be described. The patrol device 12 that patrols the facility is equipped with a five-sense sensor 124. The five-sense sensor 124 monitors the status of the production equipment. The sensing data output by the five-sense sensor 124 is transmitted to the sign detection device 3 via the communication IF 123, and in FIG. 4, an arrow schematically connects the five-sense sensor 124 to the acquisition unit 311 of the sign detection device 3. Similarly, the acquisition unit 311 also acquires process data 21 from the control station 2.

[0035] Using the acquired sensing data and process data, the anomaly degree calculation unit 312 performs a predetermined calculation according to the production equipment to be measured and the type of data to calculate the degree of anomaly. The calculation of the degree of anomaly may be performed using a trained model that has been machine-learned using past normal data and data related to abnormal modulation. For specific phenomena observed in the production equipment, such as temperature rise or abnormal noise, the degree of anomaly is calculated based on the sensing data.

[0036] The sign detection unit 313 determines whether there is a trend pattern of the degree of abnormality that matches or is similar to the trend pattern of the degree of abnormality calculated by the abnormality degree calculation unit 312, based on a combination of the assumed cause and malfunction event (the trend pattern of the degree of abnormality) that is linked to and accumulated in the causal relationship information 321. Furthermore, the cause diagnosis unit 314 identifies the assumed cause linked to the trend pattern of the degree of abnormality that is determined to match or be similar as the cause of the malfunction.

[0037] For example, the anomaly degree calculation unit 312 may determine the presence or absence of an anomaly based on the calculated anomaly degrees using a predetermined threshold as a reference, and the sign detection unit 313 may determine whether there is a trend pattern of the anomaly degrees that matches or is similar to the combination of the determination results. Furthermore, if the combination of the calculated anomaly degrees matches or is similar to the trend pattern of the anomaly degrees corresponding to multiple assumed causes, the possibility that each of the candidates may be the cause of the sign may be calculated and estimated.

[0038] 4, process data is also used to calculate the degree of abnormality and identify the cause of the malfunction. The process data is also input as a signal to the acquisition unit 311, and after undergoing the same processing as the sensing data, is processed by the sign detection unit 313. The sign detection unit 313 comprehensively judges the data from the five senses sensor 124 and the process data 21, and the cause diagnosis unit 314 identifies the occurrence of an abnormality and its cause.

[0039] The output control unit 315 drives the alarm system to issue a warning or notification if the series of processes determines that an abnormality or a sign of an abnormality has occurred in the production equipment. An example of the alarm system is a user terminal 4. The user terminal 4 may be, for example, a screen or terminal device that displays text data, an indicator that displays light, or an audio (speaker) circuit device that speaks specific information. The displayed or notified information is transmitted to the operator, user 5, who then takes action at the production equipment (site), such as replacing parts. Furthermore, if new knowledge is gained as a result of the response regarding the malfunction and its cause (causal relationship information 321), this can be further reflected in the causal relationship information 321, thereby broadening the scope of malfunction events and enabling immediate response to any abnormalities that occur. This also improves the accuracy of identifying malfunction events and their causes.

[0040] Note that signs of abnormality in production equipment and their causes may be identified using only the sensing data obtained from the signals of the five senses sensors 124 (without using the process data 21). In this case, the process data 21 in FIG. 4 is unnecessary. Also, the processor 121 in the patrol device 12 may be equipped with the functions of the sign detection device 3, the storage device 122 may store the causal relationship information 321, and the processor 121 may perform the processing of the acquisition unit 311, the abnormality degree calculation unit 312, the sign detection unit 313, the cause diagnosis unit 314, and the output control unit 315. The processing in each unit is the same as that described above, and therefore a description thereof will be omitted.

[0041] <Predictive detection processing> FIG. 5 is a process flow diagram illustrating an example of a sign detection process executed by the system. During operation of the plant 1, the sign detection device 3 repeats the process shown in FIG. 5. In the plant 1, the processor 121 of the patrol device 12 controls the drive device 125 to move along a predetermined patrol route 14, acquires sensing data generated by signal conversion from the five sense sensors 124, and stores the data in the storage device 122. The sensing data is generated in a format such as an image file, a video file, an audio file, information indicating odor intensity, odor quality (presence or absence of a specific odor), presence or absence of a substance such as a specific gas, infrared light intensity, acceleration, angular velocity, temperature, etc. The sensing data is generated, for example, when the patrol device 12 is stopped at a predetermined position or after the patrol is completed. The sensing data is recorded in association with information indicating the date and time of generation and the production equipment 11 to be inspected. The sensing data may also be associated with information indicating the location where the patrol device 12 was generated (the location where the patrol device 12 was stopped) or coordinates. Then, the processor 121 transmits the sensing data stored in the storage device 122 to the sign detection device 3 at a predetermined timing.

[0042] The processor 31 of the symptom detection device 3 acquires sensing data via the communication IF 33 (FIG. 5: S1). In this step, the processor 31 reads from the storage device 32 at least one of information indicating, for example, an image file, a video file, an audio file, the strength of an odor, the presence or absence of a specific odor, acceleration, angular velocity, temperature, etc.

[0043] After S1, the processor 31 calculates a predetermined abnormality level using the sensing data (FIG. 5: S2). In this step, a predetermined calculation is performed depending on the type of sensing data and the location where it was created to calculate the abnormality level.

[0044] When the sensing data is an image file, a video file, a heat map, or the like (i.e., a sensing signal representing light such as visible light or infrared light), a machine learning model (anomaly detection model) using an autoencoder can be used to learn the characteristics of normal image data and calculate the degree of anomaly. FIG. 6 is a diagram illustrating anomaly detection using an autoencoder. In this method, anomalies are determined using each pixel constituting an image file (or one frame of a video file) as input data. Specifically, unsupervised machine learning is performed using a neural network, for example, using normal images as training data, to create a model that can compress (encode) and restore (decode) the input data. In a neural network, for example, the number of nodes in the input and output layers corresponds to the number of pixels, and the number of nodes in the intermediate layer is smaller than the number of sensors. Information input to the input layer is compressed in the intermediate layer and restored in the output layer. Note that multiple intermediate layers may be present, and the connection structure between layers is not limited to full connection. Then, a learning process is performed using normal image data as training data, and a model is created with parameters adjusted so that the difference between the values ​​in the input layer and the values ​​in the output layer is small (the original image can be restored). In the example of Figure 6, a model is created in the learning process to obtain an output image that is nearly identical to the input image. Furthermore, in the anomaly detection process, image data, which is sensing data, is input, and the degree of anomaly is calculated based on the difference between the input layer value and the output layer value. The degree of anomaly can be calculated using L1 distance, L2 distance, SSIM (Structural SIMilarity), etc. In the example of Figure 6, image data showing an oil leak or water leakage from a mechanical seal or gland packing is input in the anomaly detection process. If image data different from normal image data is input, the information compressed in the intermediate layer cannot be properly restored in the output layer. Therefore, the difference between the values ​​of the input layer and the output layer becomes large, and the degree of anomaly is calculated based on this difference. Note that the degree of anomaly may be calculated using a one-class SVM instead of an autoencoder.Furthermore, the degree of abnormality, which is a one-dimensional index value, may be calculated based on some algorithm that evaluates the magnitude of the difference in hue, saturation, or brightness from the image data in a normal state that serves as a reference. .

[0045] When the sensing data is an audio file, the data can be decomposed into frequency components using, for example, FFT (Fast Fourier Transform) analysis as input data, and the degree of anomaly can be calculated based on the autoencoder, One Class SVM, or other algorithms mentioned above.The normal sound of an audio file also differs depending on the location (inspection object) where it was created, and the presence or absence of an anomaly can be determined based on the degree of deviation from the normal sound at each location (inspection object) (i.e., the degree of anomaly).

[0046] Even when the sensing data is information representing acceleration, angular velocity, etc. for vibration detection, the degree of abnormality can be calculated based on the above-mentioned autoencoder, one-class SVM, or other algorithms after, for example, performing a predetermined preprocessing. Information representing acceleration, etc., also has different normal characteristics depending on the location (inspection object) where it was created, and the presence or absence of an abnormality can be determined based on the degree of deviation from the normal characteristics at each location (inspection object) (i.e., the degree of abnormality).

[0047] If the sensing data is information representing odor intensity or odor quality (in other words, the occurrence of a specific substance) and its intensity, for example, the features may be learned by unsupervised learning using principal component analysis, using normal data as training data. In this case, the degree of anomaly of the sensing data can be evaluated based on its distance from the distribution of the features of the training data. If the five senses sensor 124 is an odor sensor that can detect odor quality in addition to odor intensity, the distribution of the features of the training data can be learned using information representing odor quality as a variable. The degree of anomaly may also be calculated based on the above-mentioned autoencoder, one-class SVM, or other algorithms, or the odor intensity or temperature itself may be used as the degree of anomaly. The normal characteristics of information representing odor intensity, quality, etc. also vary depending on the location (inspection target) where it was created, and the presence or absence of an anomaly can be determined based on the degree of deviation from the normal characteristics (i.e., the degree of anomaly) at each location (inspection target).

[0048] After S2, the processor 31 determines whether the combination of the above-mentioned abnormality degrees corresponds to a predetermined pattern that indicates an abnormality or a sign of an abnormality (FIG. 5: S3). In this step, the processor 31 determines whether each piece of sensing data indicates an abnormality, for example, based on the abnormality degree calculated in S2 and a predetermined threshold value for each piece of sensing data. The processor 31 also determines whether the determination result or the combination thereof for one or more pieces of sensing data matches a predetermined pattern.

[0049] FIG. 7 is a diagram illustrating an example of a judgment logic. The table in FIG. 7 includes attributes for "assumed cause" and "judgment logic," and is causal relationship information that represents the correspondence between the cause of an anomaly and the characteristics of the anomaly level. The "assumed cause" field stores information indicating the cause of an anomaly or its precursor. In the example in FIG. 7, information indicating causes such as "deterioration of pump internal parts," "deterioration of blower internal parts," "oil leak," "mechanical seal or gland packing malfunction," "pump motor deterioration," and so on are registered. The "judgment logic" also includes attributes such as "leak," "abnormal noise," "odor," "vibration," "temperature," and so on. Each item in the "judgment logic" corresponds to a type of sensing data. Each item field stores information indicating the presence or absence of an anomaly. Note that if the anomaly level exceeds a threshold value determined for the type of sensing data and the location where the sensing data was created, it is determined that an anomaly exists. Furthermore, based on the combination patterns of the presence or absence of an anomaly registered in the judgment logic, an anomaly or its precursor caused by the assumed cause of the corresponding record can be detected. The storage device 32 may further store information indicating the action that the operator should take to resolve the problem, in association with the assumed cause.

[0050] The "Leak" field stores information indicating the presence or absence of a liquid leak, determined based on visual information such as an image file. The "Abnormal Sound" field stores information indicating the presence or absence of an abnormal sound, determined based on auditory information such as an audio file. Note that information such as "pump" and "motor" in parentheses indicates the location where the audio file was created (the object of inspection). The "Odor" field stores information indicating the presence or absence of an odor, determined based on olfactory information such as odor intensity. Note that data having predetermined characteristics related to the quality of the odor, such as "process fluid" in parentheses, may be extracted, and a determination may be made based on the extracted data as to whether an odor of a strength equal to or greater than a threshold has been detected. The "Vibration" field stores information indicating the presence or absence of vibration. For example, if the degree of abnormality based on acceleration information exceeds a predetermined threshold, it is determined that vibration exists. The "Temperature" field stores information indicating the presence or absence of a temperature abnormality. For example, if the degree of abnormality based on temperature information exceeds a predetermined threshold, it is determined that there is a temperature abnormality.

[0051] The decision logic (causal relationship information) shown in FIG. 7 is an example of a knowledge base that defines the combination of causal relationships between causal events and the resulting effects, implemented in a data structure suitable for this embodiment. Such a knowledge base can be created, for example, based on HAZOP (Hazard and Operability Study). HAZOP can comprehensively correlate and list, for example, check items on a field operator's patrol route, the control range (tolerance range) for the check items, a list of assumed causes of deviations from the control range, decision logic for determining which assumed cause caused the deviation, the impact of the deviation, and countermeasures to be taken when the deviation occurs. The knowledge base can also be created based on methods other than HAZOP, such as Fault Tree Analysis (FTA), Failure Mode and Effect Analysis (FMEA), Event Tree Analysis (ETA), or methods based on these or similar methods, information extracted from operator interviews, or information extracted from work standards or technical specifications. Creating the decision logic in this way eliminates personal and experience-based differences in inspection work, enabling objective judgments. It also improves the accuracy of cause estimation based on a combination of factors. If new knowledge about causal relationships is obtained during the operation of this system, the knowledge base and the decision logic created based on it may be updated as appropriate.

[0052] If it is determined in S3 that the combination of events indicating modulation and abnormality levels matches a predetermined pattern (S3: YES), the processor 31 outputs a warning (FIG. 5: S4). In this step, the processor 31 causes an annunciator on the monitoring system to be issued from the control station 2 to the user terminal 4, for example. The warning preferably also includes information indicating the probable cause. This helps the user identify the cause, allowing the user to quickly take appropriate action according to the cause.

[0053] After S4, or if it is determined in S3 that the predetermined pattern does not match (S3: NO), the process of Fig. 5 ends. Note that the process of Fig. 5 is executed repeatedly while the plant 1 is in operation.

[0054] <Effects> If anomaly detection is performed based on process data, for example, when an abnormality appears in the flow rate of a liquid being pumped through a pipe, damage to the equipment is often already present. For example, the flow rate may be reduced due to damage to the bearings of the pump (motor), causing the motor's rotation speed to drop. Furthermore, if the motor shaft is about to seize, it may not be possible to restore the equipment by simply replacing the part, and the motor itself may have to be replaced. This increases the time required to restore the equipment, including the time required to arrange for a motor, as well as the repair costs and labor.

[0055] Using the sensing data obtained by the five senses sensor 124, for example, if a malfunction occurs in a pump that moves liquid through a pipe, abnormal noise and vibration due to malfunctioning motor bearings and a rise in temperature around the motor shaft can be detected first. In this case, the motor's rotation speed may not be significantly affected. That is, the malfunction and its cause can be detected early based on the combination of abnormalities such as sounds in unusual frequency bands and a tendency for the shaft to be too hot. Such abnormalities are difficult to detect using a flow meter (process data) attached to the pipe. Simply replacing the motor bearings can reduce the time, cost, and labor required for repair. Therefore, the ability to detect signs of abnormalities in production equipment (production devices 11) can be improved. As described above, according to the above-described embodiment, signs of motor malfunction can be detected before abnormalities appear in the process data, while the degree of the process data modulation is small, or while the number of items indicating process data modulation is small. Note that while the determination logic shown in FIG. 7 includes a combination of multiple types of sensing data, an abnormality or its signs may be detected based on a single type of sensing data. However, by using multiple types of sensing data, it is possible to improve the performance of identifying the cause, for example.

[0056] <Modification> In S3 and S4 of Fig. 5, the processor 31 may extract and output a suspected cause that matches only a part of the pattern (in other words, that has a high degree of match), not just a complete match of the pattern shown in Fig. 7. In this case, if it is not possible to narrow down the suspected cause to one, multiple possible suspected causes may be output. In this way, the user can be alerted at an early stage.

[0057] Furthermore, if multiple suspected causes are extracted, information other than sensing data may also be used to narrow down the suspected causes. Furthermore, multiple suspected causes may be displayed in descending order of likelihood, for example, based on the degree of match of the patterns shown in FIG. 7. For example, information representing the repair history of the production equipment 11 may be further stored in the storage device 32 of the symptom detection device 3, and the suspected causes may be ranked based on the timing of part replacement. In other words, a step may be included in which the more recently replaced a part is considered to be the cause, the less likely it is that the part is the cause of the malfunction.

[0058] The decision logic shown in FIG. 7 may identify the cause by combining not only the sensing data from the five senses sensor 124 but also the presence or absence of an abnormality in the process data. Furthermore, the image data described above may indirectly represent process data, such as an image of a level gauge display, such as an oil gauge. For level gauge images, an anomaly detection model can be created using supervised machine learning, for example, using images of an abnormality. FIG. 8 illustrates an example of decision logic including process data. The table in FIG. 8 also shows causal relationship information including the attributes "assumed cause" and "decision logic." Furthermore, the "decision logic" further includes the attributes "flow rate," "discharge pressure," "oil gauge," "leak," "abnormal noise," "odor," "vibration," "temperature," and so on. The items corresponding to those in FIG. 7 are not described here. "Flow rate" and "discharge pressure" correspond to process data. Furthermore, the "oil gauge" corresponds to the type of information inspected by a field operator during rounds of inspection. In the example shown in FIG. 7, the decision logic includes a confirmation that there is no change in the process data. This allows for distinguishing between the stage after an abnormality has occurred and the stage before an abnormality occurs. In other words, an alarm can be output based only on the degree of abnormality in the sensing data before an abnormality occurs in the process data. Note that an oil gauge may also be included in the process data. In this case, an alarm can be output while the number of process data items indicating an abnormality is smaller than the number of sensing data items indicating an abnormality. In either case, repairs or other measures can be taken while the impact on the production equipment 11 is still small.

[0059] Figure 9 shows an example of the logic for predicting a decline in heat exchanger performance. The heat exchanger is configured to transfer heat between a process fluid and a utility fluid. The heat exchanger is, for example, a shell-and-tube heat exchanger, where the process fluid flows through the tubes and the utility fluid flows through the shell, transferring heat through the tube walls. In the example shown in Figure 9, the following potential causes are registered: "heat exchanger fouling (process)," "heat exchanger fouling (utility)," "heat exchanger blockage (process)," "heat exchanger blockage (utility)," "internal leak in the heat exchanger," "external leak in the heat exchanger (process)," "external leak in the heat exchanger (utility)," and "deterioration of thermal insulation." An internal leak in a heat exchanger refers to the leakage of utility fluid (such as cooling water or steam) into the process side, resulting in the mixing of the heat medium used in the heat exchanger, reactor jacket, or coil, or other heat exchange equipment, into the process fluid. If contamination occurs, it may cause various abnormalities such as abnormal reactions, heat generation, and clogging. However, this embodiment makes it possible to detect early signs of abnormalities. In the example of Figure 9, the cause of the abnormality can be narrowed down with high accuracy based on the presence or absence of changes in the "flow rate" corresponding to the process data, the presence or absence of "leaks" and "odors" determined based on the sensing data, etc. In other words, signs of abnormality are detected by further combining the degree of anomaly in the process data with the sensing data. Note that if it is not possible to narrow down the suspected cause to one, multiple candidate suspected causes may be output to the user in order of likelihood.

[0060] Fig. 10 is a diagram showing an example of judgment logic for predicting a decline in the capacity of a distillation column. Note that a description of items corresponding to the judgment logic described above will be omitted. In the example of Fig. 10, in addition to the example of Fig. 9, a record for the assumed cause "leak" has been added. The presence or absence of an abnormality in a leak can be determined based on the presence or absence of a leak detected by the five senses sensor 124 and the presence or absence of an odor (the quality of the odor).

[0061] FIG. 11 is a diagram showing an example of logic for determining signs of a decline in ejector performance. Items corresponding to the aforementioned determination logic will not be described. The example in FIG. 11 further includes attributes for "thermometer (utility)" and "pressure gauge (process)" in addition to the example in FIG. 10. Records for the assumed causes "decrease in driving steam pressure" and "ejector contamination" are also added. The ejector is, for example, a steam ejector equipped with a Venturi nozzle and used to create a vacuum within the system. Signs of "decrease in driving steam pressure" and "ejector contamination" can be determined based on information similar to process data, such as a thermometer or pressure gauge. The thermometer and pressure gauge may be sensors whose scales or gauges are photographed by an imaging device, which is a five-sense sensor 124, and input as image data.

[0062] Unlike patrols by field operators, inspections of production equipment 11 using the patrol device 12 can be performed frequently. For example, the system acquires the above-mentioned data for each production equipment 11 in the morning (first time), afternoon (second time), and night (third time). At that time, the system may focus on the attribute-specific data indicated by each production equipment 11 and comprehensively assess how the data changes depending on the number of measurements to predict abnormalities and identify their causes. For example, if the first and third measurements are within the normal range and a change is observed only in the second measurement, it may be considered a coincidental phenomenon, even if there is some cause, and it may be determined that the impact on the production equipment is not significant. Conversely, if such changes occur repeatedly, it is possible that some abnormality is occurring. Another feature of the system of the present invention is that it can easily comprehensively identify abnormalities and their causes by understanding changes in data acquired at different times for the same attribute. Furthermore, increasing the measurement frequency improves the accuracy of the assessment.

[0063] The threshold for determining whether or not there is an abnormality based on the degree of abnormality may be changed depending on the season or weather. For example, if the normal temperature range of the production equipment 11 varies depending on the season, the threshold is changed accordingly. Also, for an audio file acquired in rainy weather, the threshold may be changed depending on noise. The values ​​may be changed. The season and weather may be determined based on a calendar or clock provided in the early warning detection device 3, or information obtained from other devices, or may be determined based on environmental information such as outside temperature and humidity obtained from a predetermined sensor. The patrol route 14 may be a dedicated route traveled only by the patrol device 12. In this case, the patrol device 12 can be run at any time, and if a measurement error occurs, the patrol device 12 can be returned to the location of the error at any time and remeasured. The time required for remeasurement can also be set arbitrarily.

[0064] Furthermore, the threshold for determining the presence or absence of an abnormality based on the degree of abnormality may be changed depending on the operating state of the plant 1, such as whether the plant 1 is operating steadily or non-steady. Non-steady operation includes, for example, the period from the start-up of the equipment until operation stabilizes, operation before the equipment is shut down, and any other irregular operation. For example, when the production volume per hour in the plant 1 is high or when the system is starting up, the load on the pump is high, and even if the pump temperature or vibration is relatively high, it may still be normal. In such cases, an algorithm for calculating the degree of abnormality may be created for each operating state and used separately for each operating state. Furthermore, the operating state can be determined based on a flag by appropriately controlling the flag representing the operating state.

[0065] The processor 31 monitors the accumulation status of sensing data obtained from the patrol device 12, and if data is missing for a certain time period or location (inspection target), it may instruct the patrol device 12 to recreate the data or notify the user terminal 4. When notifying the user terminal 4, for example, the operation of the patrol device 12 may be switched to manual operation, and the user terminal 4 may instruct the patrol device 12 to operate based on the operator's operation. The operator may stop the patrol device 12 at a desired position and execute one or more data acquisitions, and confirm that the data acquisition was successful based on, for example, a comparison with tolerances or past data. Furthermore, the processor 121 of the patrol device 12 may detect missing data and determine whether or not it needs to be recreated.

[0066] The patrol device 12 may record sensing data while moving, but by stopping to acquire sensing data, noise caused by movement can be reduced and the sensing data acquisition position can be standardized to improve detection accuracy. The patrol device 12 may also be capable of autonomous travel, or may operate based on user operation via the user terminal 4, for example.

[0067] At least some of the functions of the sign detection device 3 may be distributed across multiple devices (in other words, multiple processors), or the same function may be provided by multiple devices (in other words, multiple processors) operating in parallel. Also, one sign detection device 3 may be configured to include multiple processors. In other words, a system may be provided that includes one or more computers (or processors) for executing the above-mentioned processing. Also, at least some of the functions of the sign detection device 3 may be provided on a so-called cloud. Also, the patrol device 12 may calculate the degree of anomaly and send it to the sign detection device 3, or the patrol device 12 may even identify the cause of the sign and send the result to a user terminal 4, etc.

[0068] The present disclosure also includes a method and a computer program for executing the above-described process, and a computer-readable recording medium having the program recorded thereon. The recording medium having the program recorded thereon enables the above-described process by causing a computer to execute the program.

[0069] Here, a computer-readable recording medium is a medium that stores information such as data and programs electrically, magnetically, optically, mechanically, or chemically and can be read by a computer. This refers to a recording medium that can be read from or written to a computer. Among such recording media, those that can be removed from a computer include flexible disks, magneto-optical disks, optical disks, magnetic tapes, memory cards, etc. Furthermore, recording media that are fixed to a computer include HDDs, SSDs, ROMs, etc.

[0070] The configurations and combinations thereof in each embodiment and modified example are merely examples, and additions, omissions, substitutions, and other modifications of the configurations are possible as appropriate without departing from the spirit of the present disclosure. The present disclosure is not limited by the embodiments, but only by the scope of the claims. Furthermore, each aspect disclosed in this specification can be combined with any other feature disclosed in this specification. [Explanation of symbols]

[0071] 100: System 1: Plant, 11: Production equipment, 12: Patrol device, 121: Processor, 122: Storage device, 123: Communication interface, 124: Five senses sensor, 125: Driving device, 13: Access point, 14: Patrol route 2: Control station 3: Prediction detection device, 31: Processor, 32: Storage device, 33: Communication interface 4: User device

Claims

1. Acquiring sensing data representing at least one of light, sound, temperature, vibration, odor, and generation of a specific substance in a non-contact manner from production equipment including an inspection target; Calculating an abnormality level for the acquired sensing data; Detecting a sign of the abnormality and its cause based on causal relationship information indicating a correspondence relationship between the cause of the abnormality and characteristics of the abnormality level, and the abnormality level corresponding to the acquired sensing data; outputting information indicating the detected sign of the abnormality and its cause; A predictive detection system including one or more computers that execute the above.

2. There are two or more types of the acquired sensing data, and the calculation of the degree of abnormality is performed on the two or more types of the sensing data, The causal relationship information represents a correspondence relationship between a cause of the abnormality and characteristics of the abnormality level based on each of the two or more types of sensing data. The symptom detection system according to claim 1 .

3. the production facility is a chemical plant, The sensing data is data different from process data measured on a processing object of the chemical plant. The symptom detection system according to claim 1 or 2.

4. The one or more computers further acquiring the process data; Calculating an abnormality degree for the process data; Detecting a sign of the abnormality and its cause based on a combination of the degree of abnormality of the two or more types of sensing data of the inspection target of the chemical plant and the degree of abnormality of the process data. The symptom detection system according to claim 3 .

5. There are multiple inspection targets in the production equipment, Further, the system includes a moving means that is equipped with a sensor for acquiring the sensing data and that can move within the area of ​​the production facility according to a preset program; The symptom detection system according to claim 1 or 2, wherein the sensor acquires the sensing data from each of the plurality of inspection targets.

6. The moving means includes a holding mechanism that holds the sensor in a changeable orientation or position. The symptom detection system according to claim 5 .

7. An identification sign indicating that the production equipment is subject to inspection is provided on or around the production equipment, The moving means detects the identification mark by image recognition and starts acquiring the sensing data. The symptom detection system according to claim 5 .

8. When the one or more computers are unable to acquire the sensing data from the sensor, they request the means of transportation to recreate the sensing data or notify the user's terminal. The symptom detection system according to claim 5 .

9. The one or more computers acquire two or more types of the sensing data, detecting a plurality of combinations of the signs of the abnormality and their causes based on the abnormality degrees corresponding to the respective pieces of sensing data; The symptom detection system according to claim 1 or 2.

10. the one or more computers output combinations of the plurality of detected signs of anomaly and their causes in descending order of likelihood based on a degree of agreement between the characteristics of the degree of anomaly represented by the causal relationship information and the degree of anomaly corresponding to each of the acquired sensing data. The symptom detection system according to claim 9 .

11. The one or more computers store information representing the repair history of the production equipment, and for a combination of the multiple detected signs of abnormality and their causes, the more recently a part was repaired, the less likely the defect is caused by that part. The symptom detection system according to claim 9 .

12. Acquiring sensing data including at least one of light, sound, temperature, vibration, odor, and generation of a specific substance in a non-contact state from production equipment including an inspection target; Calculating an abnormality level for the acquired sensing data; Detecting a sign of the abnormality and its cause based on causal relationship information indicating a correspondence relationship between the cause of the abnormality and characteristics of the abnormality level, and the abnormality level corresponding to the acquired sensing data; outputting information indicating the detected sign of the abnormality and its cause; The method for predicting a problem is executed by one or more computers.

13. Acquiring sensing data representing at least one of light, sound, temperature, vibration, odor, and generation of a specific substance in a non-contact manner from production equipment including an inspection target; Calculating an abnormality level for the acquired sensing data; Detecting a sign of the abnormality and its cause based on causal relationship information indicating a correspondence relationship between the cause of the abnormality and characteristics of the abnormality level, and the abnormality level corresponding to the acquired sensing data; outputting information indicating the detected sign of the abnormality and its cause; A predictive detection program for causing one or more computers to execute the above.

14. Sensing data acquisition work to acquire sensing data representing at least one of light, sound, temperature, vibration, odor, and the generation of specific substances from production equipment including the inspection target in a non-contact state. The degree, a calculation step of calculating an abnormality degree for the acquired sensing data; a detection step of detecting a sign of the abnormality and its cause based on causal relationship information indicating a correspondence relationship between the cause of the abnormality and a plurality of characteristics of the abnormality level and the abnormality level corresponding to the acquired sensing data; an output step of outputting information indicating the detected sign of abnormality and its cause; a process data acquisition step of further acquiring process data measured on the processing object of the production facility, the process data being different from the sensing data; a second calculation step of calculating an abnormality degree for the process data; Including, The symptom detection method, wherein the output step is performed while the number of items of the process data indicating an abnormality is smaller than the number of items of the sensing data indicating an abnormality.

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