Abnormality diagnosis system

Abstract

To enable labor-saving and reliable abnormality diagnosis, even for a facility equipped with an irregularly and infrequently used device, such as an emergency device.SOLUTION: An abnormality diagnosis system 10 is a system for diagnosing abnormalities in a facility 13, which includes a device 12 and an engine 11 for driving the device 12. This system includes: a measurement unit 14 that is installed in the facility 13 and capable of measuring multiple pieces of data relating to the facility 13; and an abnormality diagnosis unit 15 that performs abnormality diagnosis on the facility 13 based on the multiple pieces of data measured by the measurement unit 14. The abnormality diagnosis unit 15 performs abnormality diagnosis on the facility 13 based on at least a portion of the multiple pieces of data measured by the measurement unit 14 when each device 12 is started (P1) or stopped (P3) by the engine 11.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an abnormality diagnosis system, and more particularly to an abnormality diagnosis technique for equipment equipped with a device driven by a predetermined engine. [Background technology]

[0002] For example, diesel engines are used to drive drainage pumps and emergency generators. As this type of equipment does not need to be operated constantly, it is not generally equipped with monitoring sensors. Therefore, to prevent startup problems during irregular operation, workers must periodically visit the equipment's location, for example once a month, to perform maintenance operations (no-load operation) for a few minutes to several tens of minutes and collect data.

[0003] However, with this method, the number of times data can be acquired is limited, so it is only possible to diagnose whether the equipment has started up or not, and it is difficult to acquire the amount of data necessary to diagnose the health of the equipment. Also, since the number of personnel involved in this type of management is limited, there is a limit to visiting multiple equipment installation sites to acquire data. Moreover, because actual operation occurs suddenly, it is extremely difficult to visit the equipment installation site in a timely manner, so data acquisition is essentially limited to during management operation, and it is even more difficult to collect the data actually required during steady operation.

[0004] On the other hand, an abnormality diagnosis system for mechanical equipment is known, for example, as described in Patent Document 1. That is, this diagnosis system is a system for diagnosing abnormalities in mechanical equipment such as a drainage pump, and is characterized by acquiring several types of measurement data (measurement data group) that indicate the characteristics of the mechanical equipment, performing predetermined processing on the acquired measurement data group, and then diagnosing abnormalities in the mechanical equipment based on the processed measurement data group. It also describes that the measurement data received in this case is a day's worth of measurement data such as water level information and operation information, which is stored in a database and used. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-107072 Summary of the Invention [Problem to be solved by the invention]

[0006] In this case, as described in Patent Document 1, if several types of data related to the characteristics of the above-mentioned equipment are automatically measured using sensors and abnormality diagnosis is performed based on the measured several types of data, abnormality diagnosis can be performed simultaneously on multiple equipment installed in distant locations, thereby enabling reliable abnormality diagnosis without allocating personnel as in the past. However, the above-mentioned equipment is not generally used all the time, but is used only when operation is required, such as in an emergency, and therefore is generally used irregularly and infrequently. Therefore, it has been difficult to continuously measure several types of data related to the characteristics of the equipment in the amount necessary for abnormality diagnosis, as described in Patent Document 1.

[0007] In view of the above circumstances, the technical problem to be solved in this specification is to enable highly reliable abnormality diagnosis while saving labor, even for equipment that is used irregularly and infrequently, such as for emergency use. [Means for solving the problem]

[0008] The above-mentioned problems are solved by an abnormality diagnosis system according to the present invention. Specifically, this abnormality diagnosis system is a system for diagnosing abnormalities in equipment including a device and an engine for driving the device. The system includes a measurement unit installed in the equipment and capable of measuring multiple pieces of equipment-related data, and an abnormality diagnosis unit that performs abnormality diagnosis on the equipment based on the multiple pieces of equipment data measured by the measurement unit. The abnormality diagnosis unit performs abnormality diagnosis on the equipment based on at least some of the multiple pieces of equipment data measured by the measurement unit when the equipment is started or stopped by the engine. Note that the term "engine" as used herein includes not only thermal engines such as internal combustion engines, but also machines that are powered by energy other than heat (such as electricity or fluid pressure) and can output power, such as electric motors. Furthermore, the term "equipment-related data" as used herein includes not only data related to the engine and the device that may fluctuate during operation of the equipment, but also data related to elements constituting the equipment and elements related to the equipment other than the engine and the device that may fluctuate during operation of the equipment.

[0009] As described above, the abnormality diagnosis system of the present invention focuses on the drive engine installed in the target equipment and performs abnormality diagnosis of the equipment based on multiple pieces of equipment-related data measured when the equipment is started or stopped by the engine. By performing abnormality diagnosis based on multiple pieces of data measured when the equipment is started or stopped by the engine, even equipment equipped with equipment that is not in continuous operation, such as an emergency generator, can acquire the data necessary for abnormality diagnosis during maintenance operation, for example. Therefore, abnormalities in the equipment can be detected before actual operation, preventing malfunctions when truly needed. Of course, the abnormality diagnosis system of the present invention can automatically acquire multiple pieces of equipment-related data and automatically perform abnormality diagnosis of the equipment based on the acquired multiple pieces of data, making it possible to perform highly reliable abnormality diagnosis of equipment located in remote locations.

[0010] Furthermore, in the abnormality diagnosis system according to the present invention, the abnormality diagnosis unit may diagnose abnormalities in the equipment based on at least some of the multiple data measured by the measurement unit during steady-state operation of the equipment, in addition to when the equipment is started or stopped.

[0011] In this way, by measuring multiple pieces of equipment-related data even during steady-state operation of the equipment and using the measured multiple pieces of data for abnormality diagnosis during steady-state operation, it is possible to perform abnormality diagnosis separately at startup and steady-state operation, or at shutdown and steady-state operation. Therefore, compared to performing abnormality diagnosis based on data only at startup or shutdown, it is possible to detect abnormalities in the equipment more thoroughly, and the reliability of the diagnostic results can be further improved. Furthermore, if abnormality diagnosis is performed based on separate data at startup and steady-state operation, or at shutdown and steady-state operation, it is possible to perform abnormality diagnosis using equipment-related data appropriate for each of startup and steady-state operation, or shutdown and steady-state operation, which also makes it possible to improve the reliability of the diagnostic results.

[0012] In addition, in the abnormality diagnosis system according to the present invention, the measurement unit may be triggered by the engine starting to operate, and automatically process a plurality of data measured thereafter as data measured at startup.

[0013] By configuring the measurement unit in this way, the measurement unit itself can determine that the device is in a startup state and automatically classify multiple pieces of data being measured into startup abnormality diagnosis data that can be used for abnormality diagnosis. Therefore, abnormality diagnosis at device startup can be performed accurately and smoothly.

[0014] Furthermore, in the abnormality diagnosis system according to the present invention, the measurement unit may be triggered by the passage of a predetermined time from the start of engine operation and the data relating to the engine operating state among the plurality of pieces of equipment data exceeding a predetermined value, and then automatically process the plurality of pieces of data measured thereafter as data measured during steady operation. Note that the data relating to the engine operating state here includes all parameters that can fluctuate as the engine is operated, and representative examples include the rotation speed, lubricating oil pressure, and exhaust temperature.

[0015] By configuring the measurement unit in this way, the measurement unit itself can determine when the equipment is in a steady state of operation, and automatically classify multiple pieces of data being measured into data for abnormality diagnosis during steady state operation, which can be used for abnormality diagnosis. Therefore, with this configuration, it is possible to accurately and smoothly diagnose abnormalities during steady state operation of the equipment.

[0016] In addition, in the abnormality diagnosis system according to the present invention, the measurement unit may use the stopping of the engine as a trigger to automatically process a plurality of data measured from the time the engine stops operating until a predetermined time before as data measured during the stopping operation.

[0017] By configuring the measuring unit in this way, multiple pieces of data covering a predetermined period from the time the engine is stopped to the time the engine is stopped can be used as data for diagnosing abnormalities during the stopping operation of the equipment. Therefore, with this configuration, it is possible to accurately and smoothly perform abnormality diagnosis during the stopping operation of the equipment.

[0018] In the abnormality diagnosis system according to the present invention, the abnormality diagnosis unit may diagnose an abnormality in the equipment based on the relationship between two or more predetermined pieces of data among the plurality of pieces of data measured by the measurement unit.

[0019] In this way, by performing equipment abnormality diagnosis based on the relationship between two or more predetermined data items among multiple equipment conditions measured by a measurement unit, more reliable diagnostic results can be obtained compared to when equipment abnormality diagnosis is performed based on only one type of data. In other words, when performing abnormality diagnosis based on only one type of data, the threshold setting is limited to, for example, setting allowable upper and lower limits based on accumulated data, making it difficult to detect various types of abnormalities without exception. In contrast, when performing equipment abnormality diagnosis based on the relationship between two or more predetermined data items, even if individual data items do not significantly deviate from normal values, an abnormality can be identified by determining the trend based on the combination of each type of data item (trend management). Therefore, this configuration enables equipment abnormalities to be detected more thoroughly, further improving the reliability of abnormality diagnosis.

[0020] In addition, in the abnormality diagnosis system according to the present invention, the abnormality diagnosis unit may combine all of the multiple data measured by the measurement unit in a matrix and perform abnormality diagnosis of the equipment based on each relationship between two pieces of data associated with each combination.

[0021] By combining all of the measured data in this way in a matrix and diagnosing equipment abnormalities based on the relationship between each pair of data for each combination, equipment abnormalities can be detected more reliably and without omission, thereby further increasing the reliability of abnormality diagnosis.

[0022] Furthermore, in the abnormality diagnosis system according to the present invention, when abnormality diagnosis is performed based on the relationship between data relating to two states, the abnormality diagnosis unit may display the abnormality diagnosis results performed based on each of the relationships between the two data relating to each combination in a matrix.

[0023] In this way, by displaying the abnormality diagnosis results based on the relationship between the two data for each combination in a matrix, the diagnostic pattern for each operating period of the equipment (startup, steady operation, or shutdown) can be displayed specifically and clearly. This display allows the administrator to grasp the abnormality trend at a glance, and enables more detailed abnormality diagnosis and early implementation of countermeasures.

[0024] Furthermore, in the abnormality diagnosis system according to the present invention, when abnormality diagnosis is performed based on the relationship between two pieces of data, the abnormality diagnosis unit may create a plot diagram that visualizes the relationship between the two pieces of data and perform abnormality diagnosis of the equipment based on the plot diagram.

[0025] By creating a plot diagram that visualizes the relationship between two pieces of data in this way, the plot diagram can more easily reflect the characteristics and trends specific to the equipment. Therefore, for example, by setting abnormality diagnosis criteria based on the relationship between the two pieces of data in light of the characteristics and trends specific to the equipment that appear in the plot diagram, it becomes possible to perform appropriate abnormality diagnosis for each piece of equipment.

[0026] In the abnormality diagnosis system according to the present invention, the abnormality diagnosis unit may be configured to be able to communicate with the measurement unit of each of a plurality of pieces of equipment to be diagnosed. In this case, the measurement unit of each piece of equipment may measure a plurality of pieces of data corresponding to the equipment, and the abnormality diagnosis unit may diagnose an abnormality for each piece of equipment based on the plurality of pieces of data corresponding to the equipment when starting or stopping the equipment driven by the engine provided in each piece of equipment.

[0027] The relationship between two pieces of data relating to a given combination of multiple pieces of equipment-related data may individually reflect trends according to the characteristics of the equipment, so it may not be appropriate to automatically set uniform standards for all equipment and perform abnormality diagnosis according to those standards. In light of the above, the measurement unit of each piece of equipment measures multiple pieces of data according to the equipment, and performs abnormality diagnosis for each piece of equipment based on the multiple pieces of data according to each piece of equipment, thereby making it possible to perform abnormality diagnosis taking into account the characteristics and trends unique to the equipment. Therefore, this configuration also makes it possible to perform appropriate abnormality diagnosis for each piece of equipment. [Effects of the Invention]

[0028] As described above, the abnormality diagnosis system according to the present invention makes it possible to perform highly reliable abnormality diagnosis while saving labor, even for equipment that is used irregularly and infrequently, such as for emergency use. [Brief explanation of the drawings]

[0029] [Figure 1] 1 is a diagram conceptually illustrating the overall configuration of an abnormality diagnosis system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram conceptually illustrating the configuration of a measurement unit shown in FIG. [Figure 3] 2 is a flowchart showing an example of the flow of automatic measurement by the measurement unit shown in FIG. 1. [Figure 4] 4 is a graph showing the relationship between trigger and elapsed time when steady operation is performed in the automatic measurement shown in FIG. 3. [Figure 5] 4 is a graph showing the relationship between the trigger and the elapsed time when the automatic measurement shown in FIG. 3 ends the operation before reaching steady operation. [Figure 6] 2 is an example of a plot diagram used for abnormality diagnosis by the abnormality diagnosis unit shown in FIG. 1. [Figure 7] 2 is a diagram showing the results of abnormality diagnosis by the abnormality diagnosis unit shown in FIG. 1 in a matrix form. DETAILED DESCRIPTION OF THE INVENTION

[0030] Hereinafter, the contents of an abnormality diagnosis system according to one embodiment of the present invention will be described with reference to the drawings.

[0031] Fig. 1 shows an overall configuration diagram of an abnormality diagnosis system 10 according to one embodiment of the present invention. As shown in Fig. 1, this abnormality diagnosis system 10 is capable of diagnosing abnormalities in a plurality of pieces of equipment 13 each having a predetermined engine 11 and equipment 12 driven by the engine 11, and includes a measurement unit 14 capable of measuring a plurality of states of each piece of equipment 13, and an abnormality diagnosis unit 15 that performs abnormality diagnosis on each piece of equipment 13 based on data related to the plurality of states of each piece of equipment 13 measured by the measurement unit 14. Each element will be described in detail below.

[0032] The measurement unit 14 includes, for example, a plurality of sensors 16, a data logger 17, and a control unit 18. In this embodiment, one engine 11 is connected to one device 12 so as to be capable of transmitting power, and a plurality of sensors 16 are attached to each engine 11 (see FIG. 2).

[0033] In this case, the multiple sensors 16 attached to each engine 11 are configured to be able to measure multiple data related to each engine 11. Here, the items and number of items to be measured are in principle arbitrary, and for example, if the engine 11 is an engine, the items that can be measured include rotation speed, fuel supply amount (also called rack value), fuel pressure, intake pressure, lubricating oil pressure, valve arm oil pressure, intake temperature, T / C outlet exhaust temperature, lubricating oil temperature, and coolant temperature.

[0034] In the illustrated example, the sensor 16 is configured to be capable of measuring only data related to the engine 11, but of course each sensor 16 may be configured to be capable of measuring one or more pieces of data related to not only the engine 11 but also the equipment 12. Furthermore, the sensor 16 may be configured to be capable of measuring not only the engine 11 and the equipment 12 but also one or more pieces of data related to elements of the facility 13 other than the engine 11 and the equipment 12 or elements related to the facility 13.

[0035] The data logger 17 is electrically connected to each sensor 16, performs measurements using each sensor 16, and temporarily stores the data (signals) obtained by the measurements after performing predetermined digital processing as necessary. Each stored data can be transmitted to the abnormality diagnosis unit 15 on the cloud by, for example, LTE communication via the control unit 18 (see FIG. 1).

[0036] The control unit 18 is capable of controlling the operation of the data logger 17, and is capable of controlling, for example, the execution of data measurement (period, communication to the Internet) by a predetermined sensor 16 based on a predetermined program.

[0037] 3 shows a flowchart of an example of a measurement program executed by the control unit 18. As shown in FIG. 3, this program first automatically starts measuring a plurality of pieces of data related to the equipment 13 (here, data related to a predetermined operating state of the engine 11) using the measurement unit 14 (a plurality of sensors 16) (step S1). Then, triggered by the engine 11 starting to operate, the plurality of pieces of data measured thereafter are automatically processed as a plurality of pieces of data measured at the start-up time P1 of the equipment 12. Specifically, the plurality of pieces of data related to the equipment 13 measured after receiving the trigger signal are classified as a plurality of pieces of data measured at the start-up time P1 of the equipment 12 and transmitted to the abnormality diagnosis unit 15, where they are stored and accumulated (see FIG. 4).

[0038] The trigger for starting classification of the measurement data at startup P1 can be any trigger as long as it reflects that the engine 11 has essentially started to operate. For example, if the engine 11 is an engine, a measurement item representing the operating state can be set, and the start and stop times of the engine can be determined as follows: That is, when the measurement value of the operating state is 0, the engine as the engine 11 is considered to be in a stopped state, and when the measurement value of the operating state is 1, the engine is considered to be in an operating state. The point in time when the measurement value changes from 0 to 1 is considered to be the start time of the engine, and the point in time when the measurement value changes from 1 to 0 is considered to be the stop time of the engine. In this way, the start and stop times of the engine can be automatically determined based on the numerical values ​​of the measurement values, and can be used as a trigger for starting classification into the start-up P1 data. Of course, the start and stop times of the engine 11 can also be determined based on predetermined data (e.g., operating state data of the engine 11) from among the multiple data related to the equipment 13 for which automatic measurement was started in step S1. That is, when the predetermined data exceeds a first predetermined value that has been set in advance, it may be determined that the engine 11 has started to operate, and when the predetermined operating state data falls below a second predetermined value that has been set in advance, it may be determined that the engine 11 has stopped to operate.

[0039] The above-mentioned measurement of the startup P1 data (classification of the measurement data into startup P1 data) is repeated until a predetermined time T1 has elapsed since the engine 11 starts to operate (step S4).

[0040] Then, if any of the measured data items related to the operating state of the engine 11 exceeds a predetermined value, as shown in FIG. 4 in this embodiment, if the temperature data related to the engine 11 exceeds a predetermined value D1 (step S5), this triggers the automatic processing of the subsequently measured data items as data items measured during steady operation P2 of the equipment 12 (step S6). Specifically, the multiple data items related to the equipment 13 measured after receiving the trigger signal are classified as multiple data items measured during steady operation P2 of the equipment 12 and transmitted to the abnormality diagnosis unit 15, where they are stored and accumulated. Note that in this embodiment, the case where temperature data related to the engine 11, such as T / C outlet exhaust temperature data, is used as the data related to the operating state of the engine 11 used in step S5 has been exemplified, but this is of course not limited to this. As mentioned above, data related to the operating state of the engine 11 other than exhaust temperature, such as engine speed and lubricant oil pressure, may also be used as the trigger data.

[0041] The measurement of the above-mentioned steady-state operation P2 status data (classification of measurement data into steady-state operation P2 data) is repeated until a predetermined time T2 has elapsed from the start of measurement of the steady-state operation P2 data (step S7). After the predetermined time T2 has elapsed, measurement of multiple data related to each piece of equipment 13 continues automatically. Note that, among the multiple pieces of data measured by the measurement unit 14, the type of data classified as steady-state operation P2 data may be the same as or different from the type of data classified as startup P1 data. Similarly, the number of pieces of data classified as steady-state operation P2 data may be the same as or different from the number of pieces of data classified as startup P1 data. The same applies to the types and number of status data measured during a shutdown operation P3, which will be described later.

[0042] Then, for example, when it is determined that the engine 11 has stopped operating based on the same measurement values ​​as when it started operating (step S8), this triggers the processing of a plurality of data measured from the time when the engine 11 stopped operating until a predetermined time T3 before as data P3 at the time of the stopping operation of the equipment 12 (step S9). Specifically, a plurality of data related to each piece of equipment 13 measured from the time when the trigger signal was received until the predetermined time T3 before is classified into a plurality of data measured at the time P3 at which the equipment 12 stopped operating, and is transmitted to the abnormality diagnosis unit 15, where it is stored and accumulated.

[0043] In summary, during the entire series of operations from start-up P1 of the equipment 12 (facility 13) driven by the engine 11 to steady-state operation P2 and shutdown P3, multiple data related to the facility 13 are automatically measured by the measurement unit 14 and transmitted to the abnormality diagnosis unit 15, and are stored and accumulated in a state classified into start-up P1 data, steady-state operation P2 data, and shutdown P3 data.

[0044] On the other hand, if the above-mentioned device 12 is started and then terminates its operation without reaching steady operation, the control unit 18 executes a program that controls the data logger 17 to measure multiple pieces of data related to the above-mentioned facilities 13 using a flow different from the flow described above. That is, as shown in Fig. 3, after the data logger 17 starts measuring (classifying) the startup P1 data, if the data related to the operating state of the engine 11 has not reached a predetermined value (in this embodiment, the temperature related to the engine 11 is a predetermined value D1) even when a predetermined time T1 has elapsed since the start of operation of the engine 11 (see Fig. 5), the process proceeds to step S8 without measuring the state data during steady operation P2.

[0045] Then, when it is determined that the engine 11 has stopped operating (step S8), this triggers the processing of a plurality of data measured from the time when the engine 11 stopped operating until a predetermined time T3 prior to the time (step S9) as stopping operation P3 data of the device 12. In this case as well, the plurality of data measured from the time when the engine 11 stopped operating until a predetermined time T3 prior to the time (step S9) is classified as stopping operation P3 data and transmitted to the abnormality diagnosis unit 15, where it is stored and accumulated.

[0046] As described above, during the period from the start-up P1 of the equipment 12 (facility 13) driven by the engine 11 until the completion of each operation at the stop operation P3, multiple data related to the facility 13 are automatically measured by the measurement unit 14 and transmitted to the abnormality diagnosis unit 15, and are stored and accumulated in a state classified as either start-up P1 data or stop operation P3 data.

[0047] The abnormality diagnosis unit 15 is configured to perform abnormality diagnosis for each piece of equipment 13 based on a plurality of pieces of data relating to each piece of equipment 13 measured and classified by the measurement unit 14 as described above. More specifically, the abnormality diagnosis unit 15 sets criteria (abnormality diagnosis criteria) for performing abnormality diagnosis for each piece of equipment 13 (engine 11, equipment 12) and for each state of the equipment 12 (startup P1, steady operation P2, shutdown operation P3) based on a plurality of pieces of data relating to each piece of equipment 13 (engine 11, equipment 12) measured and classified by the measurement unit 14.

[0048] In this embodiment, the abnormality diagnosis unit 15 acquires, as teacher data, a plurality of pieces of data related to each piece of equipment 13 measured during a predetermined number of operations from a plurality of pieces of data related to each piece of equipment 13 measured during the first (initial) operation after installation in each piece of equipment 13 at startup P1, steady operation P2, and shutdown P3, and sets abnormality diagnosis criteria based on the acquired teacher data. The reason for this is that data closer to the time of installation is more likely to be treated as data during normal operation.

[0049] In addition, in this embodiment, the abnormality diagnosis unit 15 sets abnormality diagnosis criteria based on the relationship between two predetermined pieces of data among the multiple pieces of data related to each piece of equipment 13 measured by the measurement unit 14, and the abnormality diagnosis program is configured to perform abnormality diagnosis of each piece of equipment 13 based on the relationship between the two predetermined pieces of data.

[0050] In this case, the abnormality diagnosis unit 15 creates a plot diagram that visualizes the relationship between the two measured data, sets abnormality diagnosis criteria for each piece of equipment 13 based on the plot diagram, and the abnormality diagnosis program is configured to perform abnormality diagnosis based on the diagnostic criteria.

[0051] Figure 6 shows an example of the plot diagram. This plot diagram shows point cloud data indicating the relationship between the engine 11 rotation speed data and the lubricant pressure data, which are part of the startup P1 data of the equipment 12 measured by the measurement unit 14 in a given facility 13. By plotting the relationship between the two data, a certain relationship (i.e., a trend) between the engine 11 rotation speed data and the lubricant pressure data in the facility 13 can be visualized. Therefore, for example, in this plot diagram (Figure 6), the range where the point cloud data is distributed can be defined as the normal range, and a predetermined range where the point cloud data is not distributed (for example, the range to the lower right and the range to the upper left of the distribution range of the point cloud data in Figure 6) can be defined as the abnormal ranges A1 and A2. In this example, the abnormal ranges A1 and A2 are further classified into a caution range A1, which is relatively close to the normal range, and a warning range A2, which is relatively far from the normal range, using an algorithm or AI learning (machine learning, deep learning).

[0052] The relationship between two predetermined data can be any combination of two of the measured data. Suitable combinations of two data for diagnosing an abnormality at startup P1 of the device 12 include the engine speed (horizontal axis) and lubricating oil pressure (vertical axis), the engine speed (horizontal axis) and valve oil pressure (vertical axis), the engine speed (horizontal axis) and fuel supply amount (vertical axis), the engine speed (horizontal axis) and intake air temperature (vertical axis), the fuel supply amount (horizontal axis) and fuel pressure (vertical axis), the elapsed time from the start of operation (horizontal axis) and fuel pressure (vertical axis), the elapsed time (horizontal axis) and lubricating oil pressure (vertical axis), the elapsed time (horizontal axis) and valve oil pressure (vertical axis), the elapsed time (horizontal axis) and fuel supply amount (vertical axis), and the elapsed time (horizontal axis) and engine speed (vertical axis).

[0053] In addition, as combinations of two data suitable for abnormality diagnosis during steady operation P2 of the device 12, the engine 11 rotation speed (horizontal axis) and fuel pressure (vertical axis), rotation speed (horizontal axis) and lubricating oil pressure (vertical axis), rotation speed (horizontal axis) and valve arm oil pressure (vertical axis), fuel supply amount (horizontal axis) and T / C outlet exhaust temperature (vertical axis), fuel supply amount (horizontal axis) and fuel pressure (vertical axis), fuel supply amount (horizontal axis) and lubricating oil pressure (vertical axis), ), fuel supply amount (horizontal axis) and valve arm oil pressure (vertical axis), fuel supply amount (horizontal axis) and engine speed (vertical axis), elapsed time (horizontal axis) and intake temperature (vertical axis), elapsed time (horizontal axis) and T / C outlet exhaust temperature (vertical axis), elapsed time (horizontal axis) and fuel pressure (vertical axis), elapsed time (horizontal axis) and lubricating oil pressure (vertical axis), elapsed time (horizontal axis) and valve arm oil pressure (vertical axis), elapsed time (horizontal axis) and engine speed (vertical axis), etc.

[0054] In addition, suitable combinations of two data for diagnosing abnormalities during the stopping operation P3 of the device 12 include rotation speed (horizontal axis) and fuel pressure (vertical axis), rotation speed (horizontal axis) and lubricating oil pressure (vertical axis), rotation speed (horizontal axis) and arm oil pressure (vertical axis), elapsed time (horizontal axis) and fuel pressure (vertical axis), elapsed time (horizontal axis) and lubricating oil pressure (vertical axis), elapsed time (horizontal axis) and arm oil pressure (vertical axis), elapsed time (horizontal axis) and fuel supply amount (vertical axis), and elapsed time (horizontal axis) and rotation speed (vertical axis).

[0055] It should be noted that the above-mentioned combinations are merely examples. It goes without saying that combinations other than those shown in the examples may also be used. Furthermore, combinations including one or more pieces of data relating to equipment 13 other than those shown in the examples may also be used.

[0056] As described above, the abnormality diagnosis unit 15 is configured to set abnormality diagnosis criteria (here, abnormality ranges A1 and A2 in the plot diagram) for all matrix combinations based on the teacher data, and to determine whether newly measured operation is abnormal based on the position of newly measured data in the plot diagram (whether the data falls within the predetermined abnormality ranges A1 and A2). In this way, the abnormality diagnosis unit 15 can perform abnormality diagnosis based on its own abnormality diagnosis criteria for each piece of equipment 13. In addition, by setting the abnormality diagnosis criteria (plot diagram similar to that of FIG. 6 and abnormality ranges A1 and A2 based on the same diagram) based on each piece of measured data classified into startup P1, steady operation P2, and shutdown P3 for each piece of equipment 13 (device 12), appropriate abnormality diagnosis can be performed based on the abnormality diagnosis criteria corresponding to each operating period (startup P1, steady operation P2, and shutdown P3). Furthermore, the results of the abnormality diagnosis performed by the abnormality diagnosis unit 15 are immediately displayed on the monitor 19 of the management company or the like connected by electrical communication means, so that, for example, the management company (manager) can quickly and reliably recognize that an operational abnormality has occurred. Note that, as a result of the automatic abnormality diagnosis performed by the abnormality diagnosis unit 15 as described above, if an abnormality is diagnosed by, for example, continuously exceeding the caution value or alarm value related to the above-mentioned judgment criteria, the management company (manager) can be notified by email of the occurrence of a caution or alarm, thereby enabling subsequent action to be taken appropriately and quickly.

[0057] As described above, the abnormality diagnosis system 10 according to this embodiment diagnoses abnormalities of each piece of equipment 13 based on multiple pieces of data related to each piece of equipment 13 measured when each piece of equipment 12 is started up (P1) or stopped (P3) by the engine 11. This allows data necessary for abnormality diagnosis to be acquired, for example, during maintenance operation, even for equipment 13 equipped with equipment 12 that is not in continuous operation, such as an emergency generator. This makes it possible to detect abnormalities in the equipment 12 prior to actual operation and prevent malfunctions when truly needed. Of course, the abnormality diagnosis system 10 according to this embodiment can automatically acquire multiple pieces of data related to each piece of equipment 13 and automatically diagnose abnormalities of each piece of equipment 13 based on the acquired multiple pieces of data. This makes it possible to perform highly reliable abnormality diagnosis on multiple pieces of equipment 13, for example, even for equipment located in remote locations.

[0058] Furthermore, in this embodiment, multiple data sets related to each facility 13 are measured during steady-state operation P2 of each device 12, and the measured multiple data sets are used for abnormality diagnosis during steady-state operation P2. This allows for separate abnormality diagnosis during startup P1 and steady-state operation P2. Therefore, compared to performing abnormality diagnosis based only on startup P1 data, abnormalities in the facility 13 can be detected more thoroughly, further improving the reliability of the diagnostic results. Furthermore, if abnormality diagnosis is performed based on separate data sets during startup P1 and steady-state operation P2, abnormality diagnosis can be performed using multiple data sets related to the facility 13 appropriate for startup P1 and steady-state operation P2, respectively. This also improves the reliability of the diagnostic results. The above-described advantageous effects can be similarly achieved when multiple data sets related to each facility 13 are measured during shutdown P3 of each device 12, and the measured multiple data sets are used for abnormality diagnosis during shutdown P3.

[0059] Furthermore, in the abnormality diagnosis system 10 according to this embodiment, when data relating to the operating state of the engine 11 among the multiple data relating to each piece of equipment 13 exceeds a predetermined value, the measurement unit 14 (control unit 18) automatically processes the multiple data measured thereafter as data measured during steady operation P2 (automatically classifying and storing the data as steady operation P2 data) (steps S4 to S6 in FIG. 3). Therefore, the measurement unit 14 itself determines that each piece of equipment 12 is in a steady operation state, and automatically classifies the multiple pieces of data being measured as data for abnormality diagnosis during steady operation P2, which can be used for abnormality diagnosis. Therefore, this configuration enables accurate and smooth abnormality diagnosis of the equipment 12 during steady operation P2.

[0060] Furthermore, in the abnormality diagnosis system 10 according to this embodiment, if a predetermined time T1 has elapsed since the start of operation of the engine 11 and data relating to the operating state of the engine 11 has not risen to a predetermined value at that time, measurement (classification) of the steady-state operation P2 data is not performed, and only measurement (classification) of the shutdown operation P3 data is performed (steps S5, S8 to S10). Therefore, a common measurement program can be used to measure (classify) and accumulate multiple pieces of data for each operating period P1 to P3 (or P1, P3), both when the device 12 is performing steady-state operation and when operation is terminated in a short time without performing steady-state operation, such as during controlled operation. Therefore, the abnormality diagnosis system 10 according to this embodiment can perform highly reliable abnormality diagnosis even when only controlled operation is performed.

[0061] Furthermore, in the abnormality diagnosis system 10 according to this embodiment, the abnormality diagnosis unit 15 performs abnormality diagnosis on each piece of equipment 13 based on the relationship between two predetermined pieces of data among the plurality of pieces of data related to each piece of equipment 13 measured by the measurement unit 14 (see FIG. 6). In this way, by performing abnormality diagnosis on each piece of equipment 13 based on the relationship between two predetermined pieces of data among the plurality of pieces of data related to each piece of equipment 13 measured by the measurement unit 14, it is possible to obtain more reliable diagnostic results than when performing abnormality diagnosis on each piece of equipment 13 based on only one type of data.

[0062] In particular, in this embodiment, when performing an abnormality diagnosis based on the relationship between two pieces of data as described above, the abnormality diagnosis unit 15 creates a plot diagram that visualizes the relationship between the two pieces of data and performs an abnormality diagnosis for each piece of equipment 13 based on the plot diagram, so that even a plot diagram related to the same combination of two pieces of data is more likely to reflect the characteristics and trends unique to each piece of equipment 13. Therefore, by setting the criteria for abnormality diagnosis (for example, the abnormality ranges A1 and A2 shown in FIG. 6 ) based on the relationship between the two pieces of data in consideration of the characteristics and trends unique to each piece of equipment 13 that appear in these plot diagrams, it becomes possible to perform a more appropriate abnormality diagnosis for each piece of equipment 13.

[0063] Although one embodiment of the present invention has been described above, the abnormality diagnosis system according to the present invention can also adopt configurations other than those described above within the scope of the gist of the system.

[0064] For example, in the above embodiment, an example was given in which an abnormality diagnosis was performed at each operating time P1 (P2, P3) based on the relationship between two predetermined pieces of data (rotation speed and lubricant pressure). However, other diagnostic modes are also possible. For example, the abnormality diagnosis unit 15 may combine all of the multiple pieces of data related to each piece of equipment 13 measured by the measurement unit 14 in a matrix and perform an abnormality diagnosis for each piece of equipment 13 based on the relationship between the two pieces of data related to each combination. When the relationship between two pieces of data is displayed in a plot diagram to set abnormality diagnosis criteria as in the above embodiment, a plot diagram showing the relationship between the data for each combination may be created, and abnormality diagnosis criteria based on the plot diagram may be set for each combination, and abnormality diagnosis may be performed for each operating period P1 (P2, P3) the number of times equal to the number of combinations.

[0065] In this way, by combining a plurality of pieces of data (here, all the data) measured by the measuring unit 14 in a matrix and diagnosing an abnormality in each piece of equipment 13 based on each relationship between two pieces of data for each combination, rather than just the relationship between two predetermined pieces of data, it is possible to more reliably detect abnormalities in each piece of equipment 13 without omission, thereby making it possible to further increase the reliability of the abnormality diagnosis.

[0066] 7 shows a diagram in which a plurality of data measured by the measurement unit 14 are combined in a matrix and the results of abnormality diagnosis performed based on the relationship between each pair of data for each combination are displayed in a matrix. In the table, a circle indicates a normal diagnosis result, a triangle indicates a diagnosis result requiring caution, and a cross indicates a warning.

[0067] In this way, by displaying the abnormality diagnosis results based on the relationship between the two pieces of data for each combination in a matrix, it is possible to specifically and clearly display the diagnosis pattern for each operation period (start-up P1, steady operation P2, or shutdown P3) of the device 12. Therefore, this display allows the administrator to grasp the abnormality tendency at a glance, and enables the administrator to quickly implement more detailed abnormality diagnosis (for example, detailed content using a plot diagram as shown in Figure 6) and countermeasures.

[0068] In the above explanation, a plot diagram is created for the relationship between two pieces of data, and the abnormal ranges A1 and A2 are set as the abnormality diagnosis criteria based on the distribution of point data that appears on the created plot diagram. However, the abnormality diagnosis criteria may be set in other ways. For example, a threshold value as an upper or lower limit may be set based on measured data, and newly measured data of the same type may be compared with the set threshold value to diagnose whether or not the operation during the measurement of the data is abnormal. In short, the form of the abnormality diagnosis criteria may be set arbitrarily, as long as abnormality diagnosis is performed based on multiple pieces of data of the equipment 13 measured by the measurement unit 14.

[0069] In addition, the above description has been given of an example in which an abnormality diagnosis of each piece of equipment 13 is performed based on the relationship between two predetermined pieces of data among the multiple pieces of data related to each piece of equipment 13 measured by the measurement unit 14, but of course this is not limited to this. For example, although a detailed description is omitted, an abnormality diagnosis of each piece of equipment 13 may be performed based on the relationship between three or more predetermined pieces of data among the multiple pieces of data related to each piece of equipment 13 measured by the measurement unit 14. [Explanation of symbols]

[0070] 10. Abnormality diagnosis system 11 Institutions 12 Equipment 13 Equipment 14 Measurement section 15 Abnormality diagnosis section 16 sensors 17 Data Logger 18 Control Unit 19 Monitor A1, A2 abnormal range P1 startup P2 During steady operation P3 During stop operation

Claims

1. A system for diagnosing abnormalities in equipment including a device and an engine for driving the device, a measuring unit provided in the facility and capable of measuring a plurality of data items related to the facility; an abnormality diagnosis unit that performs an abnormality diagnosis on the equipment based on a plurality of data measured by the measurement unit; The abnormality diagnosis unit performs an abnormality diagnosis of the equipment based on at least a portion of the multiple data measured by the measurement unit when the equipment is started or stopped by driving the engine.

2. 2. The abnormality diagnosis system according to claim 1, wherein the abnormality diagnosis unit performs an abnormality diagnosis of the facility based on at least a portion of the plurality of data measured by the measurement unit during steady-state operation of the equipment in addition to during startup or shutdown of the equipment.

3. 2. The abnormality diagnosis system according to claim 1, wherein the measurement unit is triggered by the start of operation of the engine, and automatically processes a plurality of data measured thereafter as data measured at the time of the start of operation.

4. 3. The abnormality diagnosis system according to claim 2, wherein the measurement unit is triggered by a predetermined time having elapsed since the engine started to be driven and data relating to the operating state of the engine among the plurality of data relating to the equipment exceeding a predetermined value, and automatically processes the plurality of data measured thereafter as data measured during the steady operation.

5. 2. The abnormality diagnosis system according to claim 1, wherein the measurement unit, using the stopping of the engine as a trigger, automatically processes a plurality of pieces of data measured from the time the engine is stopped until a predetermined time before the stopping of the engine as data measured at the time of the stopping operation.

6. The abnormality diagnosis system according to claim 1 , wherein the abnormality diagnosis unit performs abnormality diagnosis of the equipment based on a relationship between two or more predetermined pieces of data among the plurality of pieces of data measured by the measurement unit.

7. 7. The abnormality diagnosis system according to claim 6, wherein the abnormality diagnosis unit combines all of the plurality of data measured by the measurement unit in a matrix form and performs an abnormality diagnosis of the equipment based on each relationship between two pieces of data relating to each combination.

8. 8. The abnormality diagnosis system according to claim 7, wherein the abnormality diagnosis unit displays, in a matrix, the results of abnormality diagnosis made based on the relationship between the two data items associated with each of the combinations.

9. The abnormality diagnosis system according to claim 6 , wherein the abnormality diagnosis unit creates a plot diagram that visualizes the relationship between the two data, and performs abnormality diagnosis of the equipment based on the plot diagram.

10. the abnormality diagnosis unit is configured to be able to communicate with a measurement unit of each of the facilities as a diagnosis target for the plurality of facilities, The measurement unit of each facility measures a plurality of data items corresponding to the facility, The abnormality diagnosis system according to any one of claims 1 to 9, wherein the abnormality diagnosis unit performs an abnormality diagnosis for each piece of equipment based on a plurality of data corresponding to the equipment when the equipment is started or stopped by the engine provided in each piece of equipment.

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