WO2019082407A1 - Système de détection d'anomalie et procédé de détection d'anomalie - Google Patents

Système de détection d'anomalie et procédé de détection d'anomalie

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Publication number
WO2019082407A1
WO2019082407A1 PCT/JP2018/006601 JP2018006601W WO2019082407A1 WO 2019082407 A1 WO2019082407 A1 WO 2019082407A1 JP 2018006601 W JP2018006601 W JP 2018006601W WO 2019082407 A1 WO2019082407 A1 WO 2019082407A1
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WIPO (PCT)
Prior art keywords
abnormality
detection
operation mode
cause
mode
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PCT/JP2018/006601
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English (en)
Japanese (ja)
Inventor
遼平 松井
杉井　信之
哲史 河村
Original Assignee
株式会社日立産機システム
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Publication date
Application filed by 株式会社日立産機システム filed Critical 株式会社日立産機システム
Publication of WO2019082407A1 publication Critical patent/WO2019082407A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring

Definitions

  • the present invention relates to a diagnosis technology of an operating condition which is implemented automatically for a mechanical device or the like.
  • the present invention relates to an abnormality detection system for an apparatus or system and an abnormality detection method.
  • diagnostic operation control means for performing diagnostic operation in a plurality of modes after occurrence of an earthquake is sensed, and sound in a hoistway are collected when traveling a car in each mode of diagnostic operation.
  • Sound collecting means for producing sound
  • abnormal sound reference value storing means storing plural abnormal sound reference values set for each mode of diagnostic operation
  • An abnormal sound judging means for judging whether the sound collected by the sound collecting means is an abnormal sound based on the value and an abnormal sound in the hoistway in a plurality of travelings of the car in the same mode of the diagnostic operation Detection of an abnormal sound suitable for each mode by preventing abnormal sound information determination means for determining whether or not it has been detected at the same position of, thereby preventing erroneous detection or the like of a sound generated accidentally Is described.
  • diagnosis objects It is required to monitor the operating conditions of various machines, devices, or systems (hereinafter referred to as “diagnostic objects”) at low cost with high accuracy. For this reason, it is considered to automatically monitor the condition of the diagnosis target by attaching various sensors to the diagnosis target and analyzing data obtained from the sensors.
  • the diagnosis target operates in various operation modes.
  • analysis accuracy is deteriorated because data having largely different states is compared.
  • One aspect of the present invention is an abnormality detection system that detects an abnormality of a diagnosis target based on a detection signal from a detection element provided in the diagnosis target, the diagnosis target based on a detection signal from the detection element during normal operation
  • an abnormality cause detection unit which determines and detects the cause of the abnormality to be diagnosed.
  • An abnormality detection method for detecting an abnormality of an object to be diagnosed using an information processing apparatus comprising a storage device, an input device, a processing device, and an output device, wherein the input device is a sensor from a detection element detecting a state of the object to be diagnosed.
  • the data is acquired, and the processing device performs classification processing in which sensor data is classified for each operation mode to be diagnosed, and abnormality detection processing in which abnormality detection is performed for each classified operation mode.
  • FIG. 7 is a flow chart showing the flow of diagnosis processing performed by the edge calculation unit.
  • Block diagram showing functional blocks in the abnormality determination unit The table figure which shows the example of an abnormality cause estimation table.
  • the flowchart which shows operation
  • the conceptual diagram which shows the concept of the process which sets the threshold value for identifying a mode.
  • the flowchart which shows the operation
  • the conceptual diagram which shows the example of the driving
  • FIG. 8 is a conceptual diagram showing a state in which a sensor is mounted on an air pressure machine in Embodiment 2.
  • FIG. 8 is a conceptual diagram showing the concept of abnormality determination data of Example 2.
  • FIG. 7 is a table showing an example of an abnormality cause estimation table according to the second embodiment.
  • FIG. 14 is a conceptual diagram showing a state in which a sensor is mounted on an air pressure machine in a third embodiment.
  • FIG. 14 is a table showing an example of an abnormality cause estimation table according to the third embodiment.
  • FIG. 14 is a conceptual view showing a state in which a sensor is mounted on a cutting machine in a fourth embodiment.
  • FIG. 14 is a conceptual view showing a situation where a sensor is mounted on a semiconductor manufacturing apparatus in a fifth embodiment.
  • the layout figure which shows the example of the factory monitoring condition monitor displayed on a terminal.
  • FIG. 6 is a layout diagram showing an example of a maintenance management monitor displayed on a terminal.
  • a rotating machine that constitutes an air compressor (hereinafter referred to as "air-pressure machine") is an example of a diagnosis target, and a vibration sensor is attached to the rotating machine to remotely monitor an abnormality.
  • a specific example of a rotating machine is a motor, and a specific example of a vibration sensor is a MEMS (MEMS Micro Electro Mechanical System) sensor.
  • MEMS Micro Electro Mechanical System
  • various types of diagnostic targets and sensors can be applied.
  • the air pressure machine targeted in this example has three operation modes of stop, unload and load.
  • the stop is the state where the rotating machine is stopped
  • the unload is the state where the rotating machine is rotating but no load is applied
  • the load is the state where the rotating machine is rotating and the load is applied .
  • the state of vibration is largely different.
  • FIG. 1 is a conceptual view showing a situation where a sensor is mounted on a pneumatic device.
  • the pneumatic device 100 includes a rotating machine 102, and the rotating machine 102 compresses air in an air tank 106 via an air compression valve 104.
  • the vibration sensor 108 is retrofitted to the pneumatic device 100.
  • the attachment points are optional, and the number is also optional.
  • the pneumatics can be operated in three operating modes depending on whether the rotating machine 102 is rotating and whether the rotating machine 102 is loaded with air compression, the pneumatics can be operated in three operating modes.
  • FIG. 2 is a graph showing an example of sensor data of the vibration sensor 108 in each of the three operation modes.
  • the pneumatic device operates in three operation modes of stop, unload and load, in the three operation modes, significant differences are recognized in the acquired sensor data.
  • the horizontal axis represents time
  • the vertical axis represents acceleration.
  • the magnitude of the acceleration differs by one or more digits between stop and load / unload.
  • the pattern of the waveform is different. Therefore, high accuracy diagnosis is difficult if abnormality judgment is performed using the same judgment standard without distinguishing these data.
  • FIG. 3 is a block diagram showing the configuration of the operating condition diagnosis system of the embodiment.
  • a vibration sensor 108 is attached to the pneumatic device 100, and sensor data is transmitted to an edge computing unit 300 placed near the pneumatic device 100.
  • the edge computing unit 300 includes a storage device 301, an input device 302, a processing device (processor or CPU (Central Processing Unit)) 303, an output device 304, and a general information processing device including a bus (not shown) connecting these.
  • a processing device processor or CPU (Central Processing Unit)
  • CPU Central Processing Unit
  • a general information processing device including a bus (not shown) connecting these.
  • it can be configured by a microcomputer or a server.
  • functions such as calculation and control are realized by the processing stored in cooperation with other hardware as the program stored in the storage device 301 is executed by the processing device 303.
  • Programs executed by a computer or the like, functions thereof, or means for realizing the functions may be referred to as "function", "means", "unit", "unit”, “module” or the like.
  • the processing device 303 includes an abnormality determination unit 305 and an abnormality cause detection unit 306 as functions, and transmits the determination result of the abnormality to the central processing unit 320 via the network 310.
  • the central processing unit 320 can also be configured by a general microcomputer or server provided with a storage device 321, an input device 322, a processing device 323, an output device 324, and a bus (not shown) that connects these.
  • the central processing unit 320 includes an operation screen display unit 325, and displays the operation screen on the terminal 330 via the output device 324.
  • Communication between the vibration sensor 108 and the edge calculation unit 300 may be either wireless communication or wired communication.
  • wired communication using an I 2 C (Inter-Integrated Circuit) serial bus is assumed.
  • wireless communication such as WiFi, Bluetooth (registered trademark), ZigBee (trademark), etc. may be used.
  • the interface necessary for communication is such that the vibration sensor 108 and the input device 302 often have a known configuration.
  • the abnormality cause detection unit 306 is included in the edge calculation unit 300 in this embodiment, the abnormality cause detection unit 306 may be included in the central calculation unit 320. Further, both the abnormality determination unit 305 and the abnormality cause detection unit 306 may be included in the central processing unit 320. In that case, the edge calculating unit 300 transmits the sensor data to the central processing unit 320, not the determination result of the abnormality.
  • the above configuration may be configured as a single computer attached to a diagnosis target, or another computer in which an input device, an output device, a processing device, and any part of a storage device are connected by a network. May be composed of
  • the plurality of sensors may transmit sensor data to one edge calculating unit 300, or may transmit sensor data to another edge calculating unit (not shown). That is, although one edge calculating unit 300 is assumed in this embodiment, a plurality of edge calculating units may be provided.
  • terminal 330 Although one terminal 330 is assumed in the present embodiment, there may be a plurality of terminals. Further, communication between central processing unit 320 and terminal 330 may be either wireless communication or wired communication.
  • the operation screen display unit 325 assumes an HTML server, and codes written in languages such as JavaScript (registered trademark), CSS (Cascading Style Sheets), PHP, Ruby, Java (registered trademark), etc. Output.
  • the terminal 330 is assumed to be a tablet device which is a small computer, and it is assumed to display an operation screen on a web browser.
  • an input device in the case of a storage device, an input device, a processing device, and an output device, known general devices can be appropriately applied.
  • a magnetic disk device or various semiconductor memories can be applied as a storage device.
  • an input device a keyboard, a mouse, or various input interfaces can be applied.
  • an output device a display, a printer, or various output interfaces can be applied.
  • the function equivalent to the function configured by software can be realized also by hardware such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). Such an embodiment is also included in the scope of the present invention.
  • FPGA field programmable gate array
  • ASIC application specific integrated circuit
  • FIG. 4 is a flowchart showing the flow of diagnostic processing performed by the edge computing unit 300.
  • the vibration is measured by the vibration sensor 108 attached to the rotating machine 102 constituting the air pressure machine 100, and sensor data is acquired via the input device 302.
  • the acquired sensor data is stored in the storage device 301 as necessary for later processing.
  • the abnormality determination unit 305 determines the operation mode of the sensor data.
  • the compressed air generated by the pneumatic device 100 is stored in the air tank 106. Compressed air is sent, for example, into facilities such as a factory and is used by another mechanical device.
  • the air pressure device 100 performs discharge pressure control so that the compressed air stored in the air tank 106 maintains a constant pressure level.
  • this operation time is assumed to be normal operation time.
  • normal operation refers to an operating condition in which a device or facility operates to achieve its original purpose, and is distinguished from other operating conditions operating only for, for example, diagnosis, inspection, repair, and maintenance. Be done.
  • the air pressure machine 100 shown in FIG. 1 has three operation modes. First, there is a stop mode in which the rotating machine 102 is stopped. Next, there is an idle mode unload mode in which the rotating machine 102 is rotating but the air compression valve 104 is closed. Finally, there is a loading mode in which the air compression valve 104 opens and feeds compressed air to the air reservoir 106.
  • These three operation modes are distinguished and collected as a sensor data database (DB) 3011 for each operation mode. At this time, it is preferable to use an independent database for each operation mode.
  • DB sensor data database
  • a pneumatic device using a load / unload method is assumed as a discharge pressure control method
  • other types of air pressure machines using an on / off method, a slide valve method, and a suction throttle method are also possible. Included in the scope of the present invention.
  • the abnormality determination unit 305 determines an abnormality for each operation mode.
  • Various known methods can be adopted as a method of abnormality determination based on sensor data. For example, it is assumed to use deep learning, reinforcement learning, or a Bayesian network.
  • the feature amount to be analyzed for abnormality determination may also be a feature amount different from the operation mode determination. In general, it is desirable that the algorithm for determining the operation mode has a smaller processing amount, and the algorithm for determining an abnormality places more emphasis on accuracy than the processing amount. Moreover, it is also possible to use different algorithms or feature quantities for each operation mode.
  • FIG. 5 is a block diagram showing functional blocks in the abnormality determination unit 305.
  • the abnormality determination unit 305 includes a learning unit 3051 that learns a threshold for operating mode determination, a mode determination unit 3052 that determines the mode of sensor data using the learned threshold, and an abnormality detection unit 3053. An algorithm and a processing program used in each of these units are assumed to be effective in the processing device 303 as stored in the storage device 301.
  • the mode determination unit 3052 performs operation mode determination S402, and stores the sensor data divided for each mode as the sensor data databases 3011-1, 3011-2, and 3011-3 for each mode.
  • the abnormality detection unit 3053 detects an abnormality for each data of each mode using sensor data divided for each mode.
  • the abnormality cause detection unit 306 refers to the abnormality cause estimation table 3012 to estimate the abnormality cause.
  • eight combinations of each sensor data, normal, and abnormality form eight patterns. This combination is used to estimate the cause of abnormality of the air compressor.
  • FIG. 6 is a table showing an example of the abnormality cause estimation table 3012.
  • FIG. 6A stores the estimated cause of abnormality for each of eight patterns. It is assumed that the cause of abnormality in the abnormality cause estimation table 3012 is created and stored based on experience, experiment, or simulation by the administrator prior to the processing of FIG. 3. In addition, the cause of abnormality can be added and corrected by the administrator while operating this system.
  • the pattern 2 since an abnormality occurs even though the air pressure machine 100 is stopped, it can be estimated that the abnormality is caused by an external factor other than the air pressure machine 100.
  • pattern 3 since an abnormality occurs in a state where there is no influence of the air tank 106 serving as a load when viewed from the rotating machine 102, it can be estimated that the abnormality is caused solely by the rotating machine 102, for example, deterioration of the bearing Conceivable.
  • the pattern 4 it can be assumed that the abnormality is caused by the load, and it is conceivable that, for example, the amount of compressed air used in the plant temporarily increases and becomes an overload. As described above, by discriminating the operation pattern and judging the abnormality, the state of the device can be closely monitored.
  • the estimated cause of abnormality is transmitted from the output device 304 to the central processing unit 320 via the network 310.
  • the central processing unit 320 causes the terminal 330 to display the cause of the abnormality.
  • the sensor data is conventionally stored as one data, as shown in FIG. 5, in the present embodiment, data is stored for each operation mode, and an abnormality is determined for each.
  • data is stored for each operation mode, and an abnormality is determined for each.
  • only two patterns of normal and abnormal were obtained as a combination of data at the time of abnormal, so it was difficult to identify the cause of the abnormality. Since eight patterns are obtained in the present embodiment, highly accurate estimation of the cause of abnormality becomes possible from the combination of abnormalities.
  • the abnormality cause estimation table 3012 As a part of the abnormality cause estimation table 3012, the abnormality cause corresponding to the transition of the pattern is registered in advance. It is assumed that this is created and stored based on experience, experiments, or simulations by the administrator prior to the process of FIG.
  • the pattern transition may be left as history data in the storage device 301, and the data may be analyzed by collating with the abnormality cause estimation table 3012 periodically or at any time.
  • the operation mode determination method of the abnormality determination unit 305 in the process S402 will be described.
  • a monitoring function may be mounted in advance, but a sensor may be retrofitted. Therefore, a configuration that can identify the operation mode even with a retrofit sensor is desired.
  • the first configuration example is a configuration for acquiring device control information from a device to be diagnosed.
  • an interface that outputs a control signal which is provided in advance for a device to be diagnosed, is used to acquire the control signal to determine the operation mode.
  • This method is an ideal method that is accurate, does not need to be processed, and is excellent in real time, if the interface is available.
  • legacy devices may not output control signals, and the feasibility depends on the device to be diagnosed.
  • control information is displayed on the control panel
  • the second configuration example is a configuration to make the sensor redundant.
  • a current sensor is disposed in an electric system
  • an atmospheric pressure sensor is disposed in an air system
  • an operation mode is determined by a combination of sensor data from these.
  • calculation processing of sensor data obtained from each sensor is necessary, and setting of a threshold for that purpose is necessary.
  • the third configuration example is a configuration in which threshold value determination is performed using an effective value or an average value of sensor data.
  • the sensor is retrofitted, no additional sensor is required, and addition of hardware can be avoided, which is the most cost-effective as compared with other configuration examples.
  • it is necessary to set the threshold value by arithmetic processing and learning.
  • the configuration of the abnormality determination unit 305 illustrated in FIG. 5 corresponds to the third configuration example.
  • the operation mode is determined using data of a single retrofitted sensor.
  • the determination of the operation mode is performed by the abnormality determination unit 305 separately in the operation phase and the learning phase.
  • the mode is determined by threshold determination with small calculation cost. Therefore, first, an optimal threshold value is determined in the learning phase performed by the learning unit 3051.
  • FIG. 7 is a flow chart showing the operation of the learning phase. This function is implemented as a learning unit 3051. In the learning phase, clustering determination and threshold determination are performed on certain data, and the clustering determination compares the results of the two as a correct answer, and sets a threshold.
  • processing S701 sensor data acquired over a predetermined period is used as processing target data for learning.
  • processing target data for example, time-series sensor data of 1 minute is prepared, and this data is divided into 1 second ⁇ 60 pieces of data.
  • the number of modes assumed is acquired.
  • the number of modes may be a prescribed value if known in advance, but may be input by the user from the input device 302. In the case of a load / unload type pneumatic machine, the mode number 3 is input.
  • processing S703 clustering is performed on the processing target data to determine the mode.
  • processing such as standardization, principal component analysis, and dimension reduction is performed on the processing target data as necessary. Since these are known as data analysis methods, details are omitted.
  • Various known methods can be applied to the clustering method.
  • clusters can be determined using the k-means method.
  • the number of modes obtained in the process S 701 is used. In a simple example, an effective value and a specific frequency component can be assumed to correspond to the main component.
  • clustering As a result of the clustering determination, information of "which cluster 60 points (data) belongs to” is obtained. It is also possible to estimate information on "center of 3 clusters" and "radius of 3 clusters". In this embodiment, clustering based on a known k-means method is assumed. Generally, in the k-means clustering, in order to improve the determination accuracy, it is necessary to first determine the optimal number of clusters. That is, various clusters are input and calculation is repeated, the determination accuracy is evaluated from the determination result, and the process of selecting the optimum number of clusters is performed. Because this process requires human judgment, it is difficult to automate and the cost is high.
  • a threshold for identifying each mode is set. Since there is a threshold for each mode, processing is performed n number of modes (S704).
  • an initial value An of the threshold value is set for the mode.
  • the value may be arbitrary, for example, a value that may include several correct answers is set, and it is made to be gradually enlarged or reduced.
  • step S706 the input data is determined using the threshold set in step S705.
  • processing S 707 for each data, match / mismatch between the mode determined in the clustering in processing S 703 and the mode determined in the threshold in processing S 706 is determined, and a correct answer rate is calculated.
  • FIG. 8 shows the concept of processing for setting a threshold for identifying a mode. For example, 60 data are plotted on the plane defined by the two main components. Each data displayed as a square, a circle, and a star is a mode of data determined as a result of the clustering determination. In processing S706, the input data is determined using a threshold.
  • the threshold A2 is applied to the cluster 2.
  • the initial value A2 of the threshold is set (processing S705), threshold determination (processing S706), correct answer rate calculation (processing S707) and correct answer rate evaluation (processing S708) (Step S709).
  • FIG. 8A assumes that the threshold A2 is an initial value.
  • the initial value of the threshold A2 is defined by a radius with the central coordinates as the geometric centroid of the data of cluster 2.
  • all the data in the threshold A2 indicated by the dotted circle are cluster 2, so the correct answer rate by the correct answer rate calculation S707 is 100%.
  • the correct answer rate evaluation result in the correct answer rate evaluation S708 is regarded as NG, and the threshold A2 is updated in the process S709. In this case, the threshold A2 is increased by a predetermined number.
  • FIG. 8B shows the result of increasing the threshold A2 a predetermined number of times, and the correct answer rate is 100% (S707). Although this state is the optimum value of the threshold, in order to finally determine the optimum value, the correct answer rate evaluation result is regarded as NG (S 708), and the threshold A 2 is further increased by a predetermined number (S 709).
  • FIG. 8C shows the result of further increasing the threshold value A2 by a predetermined number. Since the data of the cluster A3 is included, the correct answer rate falls below 100%.
  • the learning unit 3051 updates the threshold An in the increasing direction, assuming that the correct answer rate evaluation is NG in a state in which the correct answer rate is 100% in the correct answer rate evaluation process S708 in the learning phase. However, if the correct answer rate falls below 100%, the threshold An is updated in the decreasing direction. Then, the threshold value An at the time when the correct answer rate reaches 100% is set as the optimum threshold value. As a result, the threshold A2 shown in FIG. 8B becomes the optimum threshold.
  • the threshold is transitioned from increase to decrease, but it is also possible to similarly set the optimum threshold by a method of transition from decrease to increase.
  • the central coordinates are fixed and the radius of the threshold is adjusted, the central coordinates can be similarly adjusted.
  • the learning unit 3051 can set thresholds (center coordinates and radius) for the three modes.
  • the calculated threshold value is appropriately updated to maintain the optimum value.
  • FIG. 9 is a flow chart showing the operation of the operation phase. This function is implemented as a mode determination unit 3052. In the operation phase, the data mode is determined using the threshold determined in the learning phase.
  • processing S901 sensor data acquired over a predetermined period is set as processing target data.
  • processing target data for example, time-series sensor data of 1 minute is prepared, and this data is divided into 1 second ⁇ 60 pieces of data.
  • the data mode is determined using the threshold determined in the learning phase. Although omitted in FIG. 9, the determination is performed in parallel or in series for each mode.
  • the mode of data is determined. That is, as the concept is explained in FIG. 8, data present within the threshold value indicated by a dotted circle is classified into, for example, three modes.
  • the classified data is stored in the storage device 301 for each mode. At this time, time information is stored in association with data.
  • FIG. 10 is a conceptual diagram showing an example of an operation schedule of the diagnostic system of the embodiment.
  • the air pressure machine 100 is basically in the normal operation state.
  • the operation mode 1002 has three modes of stop, unload, and load in the air pressure machine 100, and these can be switched at any timing.
  • the phase 1003 includes the learning phase shown in FIG. 7 and the operation phase shown in FIG.
  • the learning phase shown in FIG. 7 can be performed at any timing, but it is premised that the operation is basically normal. From this point of view, for example, when the vibration sensor 108 is installed, it is conceivable to manually activate the learning unit 3051 to enter the learning phase and learn the threshold value after confirming normal operation separately. As shown in FIG. 10, for learning, it is necessary to have a learning time enough to cover all three operation modes.
  • the threshold value set once may change due to the temporal change of the pneumatic device 100. Therefore, it is conceivable to periodically perform a learning phase, calculate a threshold, and update the threshold. However, in the learning phase, if it is not guaranteed that the air pressure machine 100 is operating normally, the slight shift of the threshold due to aging is reflected to update the threshold, but the threshold fluctuation amount exceeds the predetermined value In this case, a configuration may be considered such as not updating or generating an error signal.
  • threshold calculation and threshold update are performed in the learning phase.
  • the determination of the operation mode by the mode determination unit 3052 and the abnormality detection by the abnormality detection unit 3053 are performed.
  • a single vibration sensor 108 is used in the first embodiment, the same method can be applied even if a plurality of sensors are used. That is, one or a plurality of compressor rotation mechanisms (hereinafter referred to as rotating machines) included in one air pressure machine are measured by separate sensors, and data are stored for each sensor.
  • rotating machines there are, for example, a screw compressor, a scroll compressor, and a reciprocating compressor.
  • FIG. 11 shows an example of the air pressure machine 100 configured by the motor 102a, the first stage screw 102b, and the second stage screw 102c.
  • a sensor 108a is installed in the motor 102a
  • a sensor 108b is installed in the first stage screw 102b
  • a sensor 108c is installed in the second stage screw 102c, and each outputs sensor data.
  • each sensor data is stored in the storage device 301 as the sensor data database 3011 with the mode being determined by the same method as that described in the first embodiment.
  • the air pressure machine is generally a system operated by controlling the number of units. Therefore, even if the number control state is treated as the operation mode, the same concept as in the first embodiment is applicable.
  • FIG. 14 is a diagram showing a configuration example of a system operated by controlling the number of units.
  • the number of load absorbing machines 100-3 can be controlled by controlling the number of fixed speed two base loading machines (100-1, 100-2) and two variable speed load absorbing machines (100-3, 100-4).
  • FIG. 15 shows an abnormal data combination table stored in the abnormal cause estimation table 3012 in this case. It is possible to estimate the cause of abnormality in more detail.
  • the size of the load changes depending on the number of simultaneously moving base loading machines (100-1, 100-2) and the load absorbing machine 100-4. For example, if only the third unit 100-3 is operating and the first unit 100-1 and the third unit 100-3 are simultaneously operating, the sensor data provided to the third unit 100-3 may be different. Conceivable. Further, in the case of the vibration sensor, it is conceivable that the vibration generated from the first unit 100-1 is transmitted through the piping, the ground, etc., and affects the vibration sensor of the third unit 100-3. Therefore, high accuracy determination is made possible by distinguishing these conditions and performing abnormality determination.
  • the pneumatic device has been described as an example, but the present invention can also be applied to detection of the state of other devices.
  • An embodiment in the case of abnormality monitoring of a cutting machine will be described.
  • the cutting machine is an apparatus for cutting or grinding a work using a cutting tool, and there are, for example, a drilling machine, a milling machine, a machining center, and an NC (Numerically Control: numerical control) lathe.
  • FIG. 16 shows an example of a cutting machine provided with a drill 161 as a cutting tool.
  • the drill 161 is held by the drill holder 162.
  • the drill holding portion 162 rotates the drill 161, and the drill holding portion 162 descends, whereby the drill 161 and the work 163 come into contact with each other to perform drilling.
  • a vibration sensor 108v is attached to the drill holding portion 162.
  • a vibration sensor 108i is attached to the work 163 in order to monitor the drilling processing state.
  • the semiconductor manufacturing apparatus generally includes a vacuum chamber, and, for example, a metal deposition apparatus, a sputtering apparatus, a plasma CVD (Chemical Vapor Deposition) apparatus, an ICP (Inductively Coupled Plasma) etching apparatus There are an RIE (Reactive Ion Etching) apparatus, an ashing apparatus, an electron beam drawing apparatus, and an FIB (Focused Ion Beam) apparatus.
  • RIE Reactive Ion Etching
  • ashing apparatus an electron beam drawing apparatus
  • FIB Fluorused Ion Beam
  • semiconductor inspection apparatuses provided with a vacuum chamber, such as SEM (Scanning Electron Microscope: scanning electron microscope) or TEM (Transmission Electron Microscope: transmission electron microscope). Although there is a difference in basic configuration, it can be applied similarly as a semiconductor manufacturing apparatus.
  • FIG. 17 shows an example of the configuration of a semiconductor manufacturing apparatus provided with a roughing pump and a main drawing pump.
  • the scroll pump 171 for roughing and the turbo molecular pump 172 for main pulling are connected to the vacuum chamber 175 through the roughing valve 173 and the main pulling valve 174 respectively as shown in the drawing.
  • the scroll pump 171 is connected to the turbo molecular pump 172 through a back pressure valve 176.
  • a vibration sensor 108v and a current sensor 108i are attached to the scroll pump 171, and a pressure sensor 108p is attached to the vacuum chamber 175.
  • elements of the device not shown operate.
  • reactive gas is injected into the vacuum chamber 175.
  • Examples 1 to 5 show an example of an interface for displaying the monitoring status when a device monitor (hereinafter, user) in a factory monitors a device.
  • the user can grasp the status of the apparatus etc. through the factory monitoring status monitor displayed on the terminal 330 shown in FIG.
  • the monitor is displayed on a display of a personal computer or a display of a tablet terminal.
  • Information to be displayed is sent from the edge calculating unit 300 to the terminal 330 via the central processing unit 320.
  • FIG. 18 is an example of a factory monitoring status monitor displayed on the terminal 330.
  • the factory monitoring status monitor 181 includes an operation mode number input field 182 for setting the number of operation modes of each device, and the user can set the number of operation modes.
  • the number of modes input here is acquired in processing S702 of FIG.
  • the monitor 181 is provided with one or more device status display windows 183 for displaying the status of the device for each operation mode.
  • the abnormality determination in each operation mode is executed in process S403 of FIG. 4 and the result is sent to the terminal 330.
  • the user can know the abnormality of the apparatus by turning on the abnormality lamp 184 of the corresponding mode.
  • the maintenance proposal window 185 displays the optimal maintenance method according to the assumed cause of the abnormality.
  • the estimated cause of abnormality is estimated in the process S 404 of FIG. 4 and the estimation result is sent to the terminal 330.
  • the maintenance button 186 is pressed, a quote request mail is sent to the maintenance company via an arbitrary network.
  • Examples 1 to 5 an example of an interface in which a device monitoring person (hereinafter referred to as a vendor) of a maintenance company monitors the devices of each plant and displays the maintenance status will be shown.
  • the vendor monitors one or more factories at the same time, and can provide timely repair (or maintenance) to the user depending on the device status.
  • the hardware and software configuration are the same as in the sixth embodiment.
  • FIG. 19 is an example of the maintenance management monitor displayed on the terminal 330.
  • the vendor can grasp the device status and the maintenance status through the maintenance management monitor 191 displayed on the terminal 330 shown in FIG.
  • the device status window 192 displays an abnormal status of the device and an assumed cause.
  • the estimated cause of abnormality is estimated in the process S 404 of FIG. 4 and the estimation result is sent to the terminal 330.
  • the maintenance status window 193 displays the repair proposal status.
  • the vendor presses a mail, telephone, fax, or mail button, the user is notified of a repair proposal using each communication means.
  • the maintenance status window displays the names of the maintenance personnel in charge and the schedule of the maintenance personnel in charge.
  • the vendor can notify the user of the repair proposal after confirming the maintenance staff's schedule, which enables the prompt determination of the repair schedule.
  • the maintenance status window displays the number of stocked repair members.
  • ordering button 194 ordering information is transmitted to each member manufacturer. The vendor can manage inventory of parts necessary for repair after confirming the device status, thereby improving the efficiency of inventory management.
  • the time required for repair can be shortened by securing the stock of necessary members in advance.
  • the analysis accuracy is deteriorated because the data having largely different states is compared.
  • the resolution of each component has been reduced in order to standardize largely different data collectively.
  • abnormal data may be misjudged as normal data of another cluster.
  • the analysis accuracy is improved by performing the abnormality determination by distinguishing the operation mode using the configuration of the present embodiment.
  • diagnosis can be performed using sensor data during normal operation, diagnosis can be performed without stopping the operation. That is, since it is not necessary to provide a diagnostic operation period in particular, it is possible to prevent a decrease in the operation rate of the equipment. Further, it is possible to precisely estimate the cause of abnormality from the combination of data for each operation mode.
  • both the identification of the operation mode and the abnormality determination are performed from the sensor data, it is not necessary to introduce an additional sensor for the operation mode identification. For this reason, the introduction cost is reduced. Further, in the input / output interface screen such as the monitoring screen, the amount of information obtained by the user is increased by the presence of the indicator for displaying the normal / abnormal state for each operation mode.
  • the operation mode number input unit inputs the number of operation modes of the machine when detecting an abnormality of the machine based on a signal from a detection element provided in the machine to be diagnosed.
  • an operation mode threshold calculation unit that calculates a threshold for specifying the operation mode based on the signal of the detection element, and an operation mode specification that specifies the operation mode of the machine using the threshold based on the signal of the detection element Using a system comprising:
  • the operation mode can be determined by light processing by comparison with the threshold value.
  • OT can be used by making it possible to input the number of operation modes (for example, three types of stop, unload, and load). It is possible to improve the mode determination accuracy.
  • the present invention is not limited to the embodiments described above, but includes various modifications.
  • part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment.
  • part of the configuration of each embodiment it is possible to add, delete, and replace the configuration of another embodiment.
  • It can be used for diagnosis of driving conditions, which is implemented automatically for machines and the like.
  • abnormality determination unit 306 abnormality cause detection unit 3011: sensor data database 3012: abnormality cause estimation table 3051: learning unit 3052: mode determination unit 3053: abnormality detection unit

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

Selon la présente invention, une analyse de données de capteur est activée en tenant compte d'un mode d'excitation lors d'une excitation normale, et un diagnostic est réalisé avec une grande précision. Plus particulièrement, l'invention concerne un système de détection d'anomalie, qui détecte une anomalie d'un objet à diagnostiquer sur la base d'un signal de détection provenant d'un élément de détection fourni dans l'objet à diagnostiquer, comprenant : une unité de détermination d'anomalie qui détermine s'il existe une anomalie dans un premier mode d'excitation et un second mode d'excitation de l'objet à diagnostiquer sur la base du signal de détection provenant de l'élément de détection lors d'une excitation normale ; et une unité de détection de cause d'anomalie qui détermine et détecte la cause de l'anomalie de l'objet à diagnostiquer sur la base d'une combinaison du fait que l'anomalie existe dans le premier mode d'excitation et dans le second mode d'excitation, ce qui est déterminé par l'unité de détermination d'anomalie.
PCT/JP2018/006601 2017-10-26 2018-02-23 Système de détection d'anomalie et procédé de détection d'anomalie WO2019082407A1 (fr)

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WO2024041875A1 (fr) 2022-08-22 2024-02-29 Carl Zeiss Smt Gmbh Produit intermédiaire pour produire un élément optique pour un appareil d'exposition par projection, élément optique pour un appareil d'exposition par projection, procédé de production de produit intermédiaire et procédé de production d'élément optique

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JP7252117B2 (ja) * 2019-12-10 2023-04-04 株式会社荏原製作所 ポンプ装置及びポンプシステム
JP2022064023A (ja) * 2020-10-13 2022-04-25 株式会社荏原製作所 監視システム、監視装置及び機械監視方法
JP7125518B2 (ja) * 2021-01-27 2022-08-24 三菱重工業株式会社 多変量データの異常診断支援方法及び異常診断支援装置
JP7277504B2 (ja) * 2021-04-19 2023-05-19 株式会社日立製作所 異常検知方法及び異常検知システム
JP6992922B1 (ja) * 2021-05-11 2022-01-13 富士電機株式会社 データ分割装置、データ分割方法、及びプログラム
WO2023100314A1 (fr) * 2021-12-02 2023-06-08 株式会社Fuji Machine-outil
JP7371802B1 (ja) * 2023-01-11 2023-10-31 富士電機株式会社 異常診断システム、異常診断装置、異常診断方法、及びプログラム

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TWI704435B (zh) * 2019-08-23 2020-09-11 國立中正大學 在啟動工具機之後進行模擬確認的加工方法與加工系統
WO2024041875A1 (fr) 2022-08-22 2024-02-29 Carl Zeiss Smt Gmbh Produit intermédiaire pour produire un élément optique pour un appareil d'exposition par projection, élément optique pour un appareil d'exposition par projection, procédé de production de produit intermédiaire et procédé de production d'élément optique

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