JPWO2013030984A1 - Equipment condition monitoring method and apparatus - Google Patents

Abstract

  In case-based abnormality sign detection in equipment, it is necessary to collect various normal states comprehensively. However, the state varies greatly depending on the operation pattern such as the length of the stop time, which causes false alarms due to insufficient learning data. In order to solve the problem that it becomes difficult to execute due to the increase in memory capacity and calculation time when the learning data period is extended until the shortage of learning data is resolved, the equipment is based on the time series signal output by the equipment. In this method, a driving pattern label is assigned every fixed period based on a time-series signal, learning data is selected based on the driving pattern label every fixed period, and a normal model is based on the selected learning data. And calculate the anomaly measure based on the time series signal and the normal model, and based on the calculated anomaly measure, whether the equipment condition is abnormal It was to carry out the normal or identification.

Description

  The present invention relates to an equipment state monitoring method and apparatus for detecting an abnormality at an early stage based on multidimensional time-series data output from a plant or equipment.

  Electric power companies use waste heat from gas turbines to supply hot water for district heating, and supply high-pressure steam and low-pressure steam to factories. Petrochemical companies operate gas turbines and other power sources. Thus, in various plants and facilities using gas turbines and the like, preventive maintenance for detecting malfunctions or signs of facilities is extremely important for minimizing damage to society.

  Not only gas turbines and steam turbines, but also water turbines at hydroelectric power plants, nuclear reactors at nuclear power plants, wind turbines at wind power plants, engines of aircraft and heavy machinery, railway vehicles and tracks, escalators, elevators, equipment / parts level, Equipment that requires preventive maintenance as described above, such as on-board battery deterioration and life, has no spare time.

  For this reason, attaching a some sensor to object equipment or a plant, and judging whether it is normal or abnormal according to the monitoring standard for every sensor is performed. US Pat. No. 6,952,662 (Patent Document 1) and US Pat. No. 6,975,962 (Patent Document 2) disclose an abnormality detection method mainly for an engine. . This is because past data such as time series sensor signals are stored in a database, the degree of similarity between observation data and past learning data is calculated by an original method, and an estimated value is calculated by linear combination of data with high degree of similarity. Thus, the degree of deviation between the estimated value and the observation data is output. In JP 2010-191556 A (Patent Document 3), a compact learning data set similar to observation data is extracted from past normal data, the extracted learning data is modeled in a partial space, and observation data An anomaly detection method for detecting an anomaly based on the distance between the partial spaces is disclosed.

US Pat. No. 6,952,662 US Pat. No. 6,975,962 JP 2010-191556 A

Stephan W. Wegerich; Nonparametric modeling of vibration signal features for equipment health monitoring, Aerospace Conference, 2003. Proceedings. 2003 IEEE, Volume 7, Issue, 2003 Page (s): 3113-3121

  According to the methods described in Patent Document 1 and Patent Document 2, when observation data that is not in learning is observed by providing normal data as learning data, these can be detected as abnormal. However, the abnormality detection performance greatly depends on the quality of the learning data. That is, the observation data that is not in the learning data is judged to be abnormal even if it is normal. On the other hand, if the learning data is erroneously mixed with an abnormality, it may be judged normal even if it is abnormal. For this reason, unless learning data is collected accurately and exhaustively only in a normal state, the reliability of abnormality detection is significantly reduced. However, such learning data is collected for facilities having various normal states. It is very expensive to perform, and it is often impossible to do this manually. Furthermore, even if it is possible to collect high-quality learning data, it is a method with a high calculation load, so the amount of data allowed to be processed in a feasible calculation time is small, and as a result, completeness cannot be secured. There are many cases.

  According to the method described in Patent Document 3, the quality of learning data is equally important, but a compact learning data set similar to observation data is extracted, so that the calculation load can be reduced and the completeness can be reduced. It becomes easy to secure. However, when the data for a certain period is used as learning data, if the target is equipment with a complicated pattern of operation and stoppage, there is a case in which similar data cannot be found due to lack of learning data, resulting in a false report that determines normality as abnormal. Will occur. If the learning data period is lengthened until the shortage of learning data is resolved, the memory capacity and calculation time increase, making execution difficult. Even if the operation pattern does not change, when the state changes greatly due to maintenance work or the like, if the data for a certain period is used as learning data, the state change may be detected as an abnormality and erroneously reported.

  In addition, in the conventional case-based abnormality sign detection in facilities, it is necessary to collect various normal states comprehensively, but the state varies greatly depending on the operation pattern such as the length of the stop time, so there is a false alarm due to insufficient learning data. There is a problem that occurs. In addition, if the learning data period is lengthened until the shortage of learning data is resolved, there is a problem that memory capacity and calculation time increase, and execution becomes difficult.

  Therefore, the object of the present invention is to solve the above-mentioned problems, and to a facility having a complicated operation and stop pattern in a plant, facility, manufacturing device, measuring device (hereinafter collectively referred to as facility) or the like. Another object of the present invention is to provide an equipment state monitoring method and system including an abnormality detection method capable of detecting an abnormality with high sensitivity while keeping a calculation load low. Furthermore, in addition to changes in operation and stop patterns, there is also a facility status monitoring method and apparatus that can realize highly sensitive abnormality detection by selecting appropriate learning data when there is a change in state due to maintenance work. It is to provide.

  In order to achieve the above object, in the present invention, in a method for monitoring the state of equipment based on a time-series signal output from the equipment, an operation pattern label is assigned every fixed period based on the time-series signal, Learning data is selected based on each driving pattern label, a normal model is created based on the selected learning data, an anomaly measure is calculated based on the time series signal and the normal model, and an equipment measure is calculated based on the calculated anomaly measure. The status was identified as abnormal or normal.

  Further, in the present invention, in the method for monitoring the state of the equipment based on the time series signal output from the equipment or the apparatus, the operation pattern label for assigning the operation pattern label categorized in a finite number to the time series signal for every fixed period A data accumulation process for accumulating as a data a time series signal to which an operation pattern label has been imparted by the operation pattern label imparting process, and a time series signal from among data accumulated in the data accumulation process. A learning data selection step that selects a predetermined number of data based on the driving pattern label and sets it as learning data, a normal model creation step that creates a normal model using the learning data selected in this learning data selection step, and this normal An anomaly measure that calculates an anomaly measure of time series signals based on comparison with normal models created in the model creation process And output process, and to include an abnormality identification step of identifying an abnormality based on the abnormality measure calculated in this anomaly measure calculating step.

  In the present invention, a sensor signal analysis unit that inputs and analyzes a time-series signal output from the facility or apparatus, and an abnormality of the facility is diagnosed by receiving the result analyzed by the sensor signal analysis unit and the time-series signal. An apparatus for monitoring an equipment state having an abnormality diagnosis unit, an input / output unit for inputting and outputting data connected to the sensor signal analysis unit and the abnormality diagnosis unit, and the sensor signal analysis unit outputs from the facility or the apparatus Based on the time-series signal, driving pattern label attaching means for giving driving pattern labels every predetermined period, and learning data is selected based on driving pattern labels for every predetermined period given by the driving pattern label attaching means Learning data creation means, normal model creation means for creating a normal model based on the learning data created by the learning data creation means, and the normal model creation An abnormal measure calculating means for calculating an abnormal measure of a time-series signal output from a facility or apparatus based on a normal model created by the means, and the equipment state is abnormal or normal based on the abnormal measure calculated by the abnormal measure calculating means And an abnormality identification means for identifying the above.

  Further, according to the present invention, in a device for monitoring a facility state by inputting a time-series signal output from the facility or apparatus, the time-series signal is input and externally input and categorized into a finite number of fixed time intervals. The operation pattern label attaching means for assigning the operated pattern label, the data storage means for accumulating the time series signal given the operation pattern label by the operation pattern label attaching means, and the time series signal by the operation pattern label attaching means A learning data selecting means for selecting learning data by selecting a predetermined number of data of the same driving pattern label or a driving pattern label in a similar state from the data accumulated in the data accumulating means based on the given label, and the learning data Normal model creation means for creating a normal model using the learning data selected by the selection means, and driving An abnormal measure calculating means for calculating an abnormal measure of the time series signal by comparing the time series signal given the driving pattern label by the turn label attaching means with the normal model created by the normal model creating means, and the abnormal measure calculating means And an abnormality identifying means for identifying abnormality of the time-series signal given the driving pattern label based on the calculated abnormality measure.

  According to the present invention, an operation pattern label is assigned to data every fixed period, and when an abnormality is detected, a predetermined number of data of the same label or a label with a similar state is collected as learning data, thereby increasing the calculation load. Therefore, it is possible to create a normal model with high accuracy and reduce false alarms that determine normality as abnormal.

  Furthermore, according to the present invention, a macro feature representing a macro variation of sensor data is calculated, and a predetermined number of data of the same label or a label having a similar state and a similar period of the macro feature are collected and used as learning data. Even when the state changes greatly due to the above, it becomes possible to select similar learning data and create a normal model with high accuracy, and to reduce false alarms for determining normality as abnormal.

  As described above, not only equipment such as gas turbines and steam turbines, but also water turbines in hydroelectric power plants, nuclear reactors in nuclear power plants, wind turbines in wind power plants, aircraft and heavy machinery engines, railway vehicles and tracks, escalators, elevators, At the equipment / part level, a system that can simultaneously detect a highly sensitive abnormality and easily explain the abnormality in various facilities / parts such as deterioration and life of the on-board battery can be realized.

It is a block diagram which shows the structure of the outline of the installation condition monitoring system in 1st Example of this invention. It is a signal list | wrist which shows the example of the sensor signal in 1st Example of this invention. It is a signal list | wrist which shows the example of the event signal in 1st Example of this invention. It is a flowchart which shows the flow of a process of operation pattern label provision in the 1st Example of this invention. It is a schematic diagram of the mode division | segmentation which shows the state which divided | segmented the movable state of the installation in 1st Example of this invention, and was classified into either of 4 types of modes. It is a figure explaining the concept of driving | operation pattern label provision in 1st Example of this invention. It is a figure which shows the example of the relationship between the data calculated from the event signal in the 1st Example of this invention, and an operation pattern label. It is a data list which shows the example of the management data of the driving pattern label in 1st Example of this invention. It is a data list which shows the example of the sensor signal data in 1st Example of this invention. It is a data list which shows the example of the event signal data in 1st Example of this invention. It is a data list which shows another example of the management data of the driving pattern label in 1st Example of this invention. It is an example of the information input screen for determining the conditions for labeling in the first embodiment of the present invention. It is an example of the detailed information input screen for determining the conditions for labeling in the first embodiment of the present invention. It is an example of the screen which confirms the relationship between the reference information and label in 1st Example of this invention. It is an example of the screen which displays the label in the 1st Example of this invention, label provision reference information, and a sensor signal in a graph. It is a flowchart which shows the flow of the process of the learning data selection in 1st Example of this invention. It is a flowchart which shows the flow of a normal model production | generation process in 1st Example of this invention. It is a graph explaining the projection distance method. It is an affine subspace figure explaining a local subspace method. It is a block diagram which shows the structure of the outline of the equipment state monitoring system in 2nd Example of this invention. It is an example of the information input screen for determining the conditions for labeling in the second embodiment of the present invention. It is a flowchart which shows the flow of the process of the learning data selection in 2nd Example of this invention.

  Hereinafter, the contents of the present invention will be described in detail with reference to the drawings.

  FIG. 1 shows a configuration example of a system that realizes the equipment state monitoring method of the present invention. The system includes a sensor signal analysis unit 100, an abnormality diagnosis unit 120, and an input / output unit 130.

  The sensor signal analysis unit 100 of this system includes an operation pattern label assigning unit 104 that assigns an operation pattern label every predetermined period based on the sensor signal 102 and the event signal 103 output from the equipment 101, and a sensor to which the operation pattern label is assigned. A database 105 for storing the signal 102 and the event signal 103, a learning data selection unit 106 for receiving the sensor signal 102 and the event signal 103 and selecting learning data from the sensor signal 102 and the event signal 103 stored based on the driving pattern label. A normal model creation unit 107 that creates a normal model using the selected learning data, an abnormal measure calculation unit 108 that calculates an abnormal measure based on the normal model and the sensor signal, and an abnormality identification that detects an abnormality based on the calculated abnormal measure Part 109.

  The abnormality diagnosis unit 120 receives the sensor signal 102, the event signal 103, and the output from the abnormality identification unit 109, and diagnoses the abnormality of the equipment 101.

  The input / output unit 130 is connected to the driving pattern label applying unit 104, the abnormality identifying unit 109, the abnormality diagnosing unit 120, etc., and inputs diagnostic conditions and outputs diagnostic results.

  The equipment 101 subject to state monitoring is equipment or a plant such as a gas turbine or a steam turbine. The facility 101 outputs a sensor signal 102 and an event signal 103 representing the state.

  An example of sensor signal 102 is shown in FIG. 2A. It is a time-series signal that consists of time and data of a plurality of sensor values provided at 101 and is acquired at regular intervals. There are cases where the number of types of sensors is from hundreds to thousands, for example, the temperature of cylinders, oil, cooling water, etc., the pressure of oil or cooling water, the rotational speed of the shaft, the room temperature, the operating time, etc. is there. In addition to representing an output or state, there may be a control signal for controlling something to a certain value.

  An example of the event signal 103 is shown in FIG. 2B. This is an irregularly output signal indicating operation / failure / warning of equipment, and consists of a time, a unique code indicating operation / failure / warning, and a message string.

  Next, the configuration and operation of the sensor signal analysis unit will be described with reference to FIGS. First, the flow of processing in the driving pattern label assigning unit 104 will be described with reference to FIGS. 3A and 3B. First, the event signal 103 is input (S301), and a start sequence and a stop sequence are cut out by searching for a predetermined character string or code (S302). Based on the result, as shown in FIG. 3B, from the end time of the stop sequence to the start time of the start sequence, the “steady OFF” mode 311, the “start” mode 312 in the start sequence, and the end time of the start sequence The operation is divided into four operating states: a “steady ON” mode 313 up to the start time of the stop sequence and a “stop” mode 314 in the stop sequence (S303). In the following description, this division is referred to as mode division, and the type of operating state is referred to as mode.

In order to cut out a sequence, a start event and an end event of a sequence are designated in advance, and the event signal 103 is cut out while scanning from the beginning to the end in the following manner.
(1) If it is not in the middle of the sequence, search for a start event. When it is found, the sequence starts.
(2) In the middle of a sequence, search for an end event. If found, end the sequence.
Here, the end event is a specified end event, a failure, a warning, or a specified start event. If it is terminated at a non-specified end event, it is recorded as an abnormal end.

  Next, the number of state transitions is counted every predetermined period, for example, every day (S304). State transition means a transition between steady ON and steady OFF. That is, the number of start sequences and stop sequences may be counted. Next, it is checked whether the initial state of the predetermined period is ON or OFF, and the duration of that state up to that point is calculated (S305). The closest start sequence or stop sequence before that point is searched, and if it is a start sequence, the state is ON, and if it is a stop sequence, the state is OFF. For the duration, the time from the end of the sequence may be calculated. Next, an operation pattern label is assigned based on the calculated number of state transitions, the initial state of a predetermined period and its duration (S306).

  An operation pattern label applying method will be described with reference to FIGS. 4A and 4B. FIG. 4A is a time series graph representing information on equipment ON / OFF and one sensor signal. It can be seen that the sensor value varies greatly depending on the state duration and the state transition pattern as well as the sensor value varies depending on whether it is ON or OFF. Since the purpose of operation pattern labeling is to select data with similar equipment conditions as learning data, for example, the same label is assigned to the data surrounded by the solid line, dotted line, broken line, and alternate long and short dash line shown in FIG. 4A. To do. For this purpose, the number of state transitions calculated in S304 and S305, the initial state of a predetermined period, and its duration are used.

  FIG. 4B shows an example of the relationship between the information and the driving pattern label. As the number of state transitions increases from 0 to 1, 2,..., Different labels are assigned. However, in order to make the types of labels finite, they are treated the same for a predetermined number of times. In this example, five or more times are combined into one. Further, since the continuous time is continuous data, the data is classified by providing an appropriate delimiter so that a different label is assigned to each. Further, different labels are assigned depending on whether the initial state is ON or OFF. Look at the corresponding columns for the number of state transitions and the duration of the initial state. If the initial state is ON, the label on the left (1-36 in the example of FIG. 4B), if OFF, the label on the right (Figure In the example of 4B, labels 37 to 72) are given.

  Note that the method of dividing the horizontal axis and the vertical axis may be finer or coarser, or all information may not be used. Further, other information may be added for classification. Moreover, it is not necessary to assign different labels to all the columns. In this example, there are two types of the initial state, ON or OFF, but it is possible to distinguish between complete stop and idling state even during OFF, and to distinguish between different operation modes during ON. It is important to use information related to whether or not the state of the equipment is similar, and is not limited to the items listed here. For example, it can be considered that the presence or absence of maintenance work or the presence or absence of warning is reflected on the label.

  Further, in the present embodiment, an example in which mode division is performed using an event signal has been described, but there may be a case where the sensor signal includes a signal indicating ON / OFF of the facility or other operation state. In that case, the information may be used to calculate other information related to whether the number of state transitions, the initial state of a predetermined period, and the duration or state of equipment are similar.

  The sensor signal 102 and the event signal 103 to which the operation pattern label is assigned are stored in the database 105. An example of a data management method in the database 105 will be described with reference to FIGS. 5A to 5D.

  FIG. 5A is an example of operation pattern label management data. In this example, the above-mentioned predetermined period is from 0 o'clock to 24 o'clock in the day, and for each date, site (code indicating the installation location), and unit (ID for distinguishing a plurality of facilities in the same location) Has one piece of data. This data includes date 501, site 502, unit 503, operation pattern label 504, initial state 506 used to assign key code 505 and operation pattern label to sensor signal data and event signal data, continuation of initial state It consists of time 507, state transition count 508, and warning presence / absence information 509. Items 506 to 508 vary depending on the method of applying the operation pattern label. Further, when there is one facility connected to the system, the items of the site 502 and the number machine 503 are unnecessary.

  FIG. 5B shows sensor signal data. Although having the same information as the output 102 from the facility 101, a key code 511 associated with the key code 505 to the sensor signal data and event signal data of the management data shown in FIG. 5A is added. Since the date is uniquely determined when the key code is determined, the date information may not be included. The time information 512 is necessary for association with the event signal. However, when it is guaranteed that the data is at regular intervals, serial numbers in the order of acquisition may be used instead of the time information. FIG. 5C shows event signal data. It consists of the same code 523 and message 524 as the output 103 from the equipment 101, the key code 521 associated with the key code 505 of the management data, and time information 522. Since the date is uniquely determined when the key code is determined, the date information may not be included.

  The operation pattern label management period does not always start at 0 o'clock, and may be divided at an arbitrary time. In addition, the length of the period is not limited to one day. In order to prevent the labeling from becoming complicated, it is advisable to align with the main repetition cycle. For example, a power generation facility using a gas engine is appropriately set to one day because it is operated in accordance with the operation cycle of the facility where the facility is installed, but every 12 hours or every 8 hours may be considered. By shortening the period, the number of states will decrease and it will be easier to collect data in the same state. However, if the number of pieces of sensor data to be learned is kept constant, the number of management data to be searched and the number of pieces of management data to be collected when selecting the sensor data to be learned increase. Conversely, it is possible to lengthen the period, but it should be noted that the number of states may increase and it may be difficult to collect data in the same state.

  Furthermore, it is conceivable that the operation pattern label management period is divided not only at a fixed time but also at the timing of state transition. FIG. 5D shows an example of operation pattern label management data in that case. This data includes date 531, site 532, number 533, operation pattern label 534, key code 535 to sensor signal data and event signal data, and warning presence / absence information 539 as in the example shown in FIG. 5A. Since new data is created when there is a state transition, the data used for labeling has information on the state 536 before the management period, the duration 537 of the state, and the state 538 of the management period. Assuming that the number of classes in the duration of the previous state is 6, which is the same as above, the number of classes in the previous state and the current state is 2, so the number of labels can be managed as small as 24. However, since the number of sensor signal data associated with each management data is different, the number of sensor signals should be set to a predetermined number instead of the number of management data when selecting learning data. There is a demerit that the process becomes complicated.

  FIGS. 6A to 6D show examples of GUIs for determining the length of the period during which the operation pattern label is given and the rule for labeling. The GUI is displayed on the display screen 131 of the input / output unit 130. First, although not shown in the drawing, a sensor signal and an event signal to be used as a reference for determining a labeling rule are selected and loaded. Next, using the input screen shown in FIG. 6A, the information classification method used for labeling is determined. FIG. 6A is an example of an input screen for selecting information used for operation pattern label assignment. The operation pattern label addition reference information input window 601 includes a cycle information input window 602, a cycle unit input window 603, reference information selection windows 604A to 604C, class number input windows 607A to 607C, and the like. In the period information input window 602, a numerical value is input for a period during which an operation pattern label is assigned. In the period unit input window 603, the unit of the period is selected and input from time, day, and week. In the reference information selection windows 604A to 604C, information used for labeling is input.

  It is assumed that usable information is determined in advance, and the referenceable information list 606 is displayed when the list display buttons 605A to 605C are clicked. In the class number input windows 607A to 607C, the number of classes to which different labels should be assigned based on the referenced information is input. There are two types of referenceable information: continuous data and nominal data. In the case of nominal data, the nominal number is input as the number of classes in the class number input windows 607A to 607C, and user input is not accepted. Further, the detail buttons 608A to 608C are inactivated.

  In the case of continuous data, the number of classes is numerically input in the class number input windows 607A to 607C. It is an integer value of 2 or more, and the upper limit is not particularly determined, but is set to an appropriate value such as 10, 100, for example. In the case of continuous data, when any of the detail buttons 608A to 608C is pressed, an operation pattern label addition reference information detail input window 611 shown in FIG. 6B is displayed, and detailed information for defining class boundaries is input. An add button 609 is a button for making the information to be referred to be 4 or more. When the add button 609 is pressed, a reference information selection window 604 corresponding to information 4, information 5,..., A list display button 605, the number of classes An input window 607 and a detail button 608 are sequentially displayed. When the input including the detailed information is completed, the label definition button 610 is pressed. By this operation, the label confirmation screen shown in FIG. 6C is displayed.

  FIG. 6B is an example of a class boundary input screen for reference information corresponding to any of the pressed detail buttons 608A to 608C. Since continuous data is targeted, class boundary values are entered. The operation pattern label addition reference information detail input window 611 includes a reference information display window 612, a numerical value range display window 613, and class boundary value input windows 614A to 614E. In the reference information display window 612, the name of the reference information corresponding to any of the pressed detail buttons 608A to 608C is displayed. In the figure, an example in which the details button 608B is pressed in the driving pattern label addition reference information input window 601 is shown. In the numerical value range display window 613, the range of the value of the operation pattern label addition reference information calculated for the event signal 103 loaded in advance under the condition of the input cycle is displayed.

  The class boundary value input windows 614A to 614E are displayed by a number one less than the numerical value input in the class number input windows 607A to 607C. When the number of classes is 6, if x is less than x, a numerical value is input for x which is a condition for determining a state of class N (N is 1 to 5). In the example of the figure, if it is less than 1, it is class 1, that is, if the number of state transitions is 0, it is class 1. If it is 1 or more and less than 2, it is class 2, that is, if the number of state transitions is 1, it is class 2. Similarly, up to class 5 is defined, and the condition of class 6 is other cases, that is, the case where the number of state transitions is 5 or more. After the input is completed, when the end button 615 is pressed, the operation pattern label addition reference information input window 601 is returned to.

  FIG. 6C is an example of a confirmation screen for a label defined according to the input result of FIG. 6A. The operation pattern label confirmation window 621 includes label display windows 622A and 622B and a class boundary condition display window 626. The label display windows 622A and 622B are two-dimensional matrices, and display the label numbers corresponding to the information classes shown in the horizontal axis items 624A and 624B and the vertical axis items 625A and 625B when the conditions are 623A and 623B. It is. In the horizontal axis items 624A and 624B and the vertical axis items 625A and 625B, the number of windows 622 can be minimized by selecting and setting two reference information items having a large number of classes. In the conditions 623A and 623B, all the remaining combinations of reference information classes are described separately.

  The displayed label is automatically determined by the following method based on the combination of classes of information. When the class C1 of information 1, the class C2 of information 2, and the class C3 of information 3 are written as (C1, C2, C3), (1, 1, 1) starts from (1, 1, 2), (1 , 1, 3),... (1, 1, 6), (1, 2, 1),..., (2, 6, 6), a serial number starting from 1 is assigned as a label. . In this case, since the number of classes of information 1 is 2, the number of classes of information 2 is 6, and the number of classes of information 3 is 6, the total number of labels is 72. When referring to the information 4, all combinations of (C1, C2, C3, C4) are sequentially counted and given serial numbers. The same applies when referring to other information. In the class boundary condition display window 626, the item name of the reference information and the condition for defining the class corresponding thereto are displayed.

  When the reference information is nominal data with respect to the reference information set in the operation pattern label addition reference information input window 601 shown in FIG. 6A, each name is individually assigned to a class and displayed. When the reference information is continuous data Displays the class boundary condition input in the operation pattern label addition reference information detailed input window 611 shown in FIG. 6B as it is. When the graph display button 627 is pressed, a graph display window 631 shown in FIG. 6D is displayed. By depressing the registration button 628, the labeling conditions are confirmed and registered. When the return button 629 is pressed, the operation pattern label addition reference information input window 601 shown in FIG.

  FIG. 6D is an example of a graph display screen for confirming a label assigned based on an input condition in association with sensor data. The graph display window 631 includes a label addition reference information display window 632, an ON / OFF information display window 633, and a sensor signal display window 634.

  Prior to the graph display, a driving pattern label is given by the processing flow shown in FIG. 3A using the sensor signal and the event signal loaded in advance. In the label addition reference information display window 632, information referred to for label assignment, that is, the class number of the initial state, the number of state transitions, and the initial state duration is displayed in a line graph, and the label number is displayed in an overlapping manner. .

  In the ON / OFF information display window 633, ON / OFF information obtained by mode division (S303) is displayed as a time series graph. In the sensor signal display window 634, the sensor signal selected by the sensor selection window 637 is displayed as a time series graph. The period of data displayed in the window is changed by moving the scroll bar 635. The period display window 636 displays the period of the data being displayed.

  Conversely, the scroll bar 635 moves according to the input by the user, and the display of the windows 631, 632, 633 is changed. When the return button 638 is pressed, the graph display window 631 is deleted and the operation pattern label confirmation window 621 shown in FIG. 6C is returned to.

  With the above-described GUI, it is possible to easily input labeling conditions. Since it is possible to observe the label and sensor data given using the determined conditions in association with each other, it is possible to check whether the equipment states in the period when the same label is given are similar to each other. It is possible to confirm the validity of the labeling conditions.

  Next, the flow of processing in the learning data selection unit 106 will be described with reference to FIG. First, the sensor signal 102 and the event signal 103 are input to the driving pattern label applying unit 104 (S701), and the driving pattern label applying unit 104 applies the driving pattern label for each predetermined period (S702). Next, the learning data selection unit 106 performs learning data selection processing according to the following procedure. First, the same label as the assigned label is set as a search target (S703), and operation pattern label management data of the same machine at the same site is loaded from the database 105 (S704). Next, the search target label is searched (S705), and the search result is recorded (S706). However, data with warning is not searched. In addition, an exclusion date may be specified by a file or a user interface so that it can be removed from the search target. Further, it is confirmed whether or not the number of searched data has reached a predetermined number (S707), and if not, a search target label is set (S708), and the process returns to step S705.

  For each operation pattern label, other labels that are expected to contain similar equipment states are determined in advance with priorities, and in step S708, search target labels are determined according to the priorities. This is repeated until the learning data reaches a predetermined number, and sensor signal data and event signal data associated with the key code described in the searched driving pattern label management data are loaded in chronological order (S709). However, the amount exceeding the predetermined number in the last search is selected in order from the newest label in the last search target label so that the total becomes the predetermined number.

  Through the above processing, learning data similar to input data can be selected from all the accumulated data. As a result, it is possible to identify anomalies with high accuracy, and to reduce misinformation caused by a lack of learning data. At this time, since the number of learning data is limited to a predetermined number, the calculation time does not increase.

  Next, the flow of processing in the normal model creation unit 107 will be described with reference to FIG. First, the sensor signal data and event signal data selected by the learning data selection unit 106 are input as learning data (S801). Next, feature extraction is performed to create a feature vector (S802), which is divided into separate groups according to the key codes of sensor signal data and event signal data (S803), and learning is performed using data excluding one group. The normal model is created (S804). Using the created normal model, the abnormal measure is calculated by inputting the data of one group removed in step S804 (S805). It is checked whether the calculation of the abnormal measure has been completed for the data of all groups (S806). If the group has not been calculated yet (S807), the normal model is created (S804) and the abnormal measure is calculated. The step of (S805) is repeated. When calculation of the abnormality measure is completed for all groups of data (S806), a threshold value for identifying abnormality is set based on the calculated abnormality measure (S808). Finally, a normal model is created using all the learning data (S809).

Next, each step will be described in detail.
First, in step S801, a sensor signal is input, and in step S802, feature extraction is performed to obtain a feature vector. For feature extraction, it is conceivable to use the sensor signal as it is, but a window of ± 1, ± 2, ... is provided for a certain time, and the feature vector of window width (3, 5, ...) x number of sensors It is also possible to extract features that represent changes in data over time. Also, discrete wavelet transform (DWT: Discrete Wavelet Transform) may be applied to decompose into frequency components. Each feature may be canonically converted using an average and standard deviation so that the average is 0 and the variance is 1. The average and standard deviation of each feature is stored so that the same conversion can be performed at the time of evaluation. Or you may normalize using the maximum value and minimum value, or the preset upper limit and lower limit.

  These processes are for simultaneously handling sensor signals of different units and scales. At this time, as a minimum process, it is necessary to exclude a sensor signal having a very small variance and a monotonously increasing sensor signal.

  It is also conceivable to delete invalid signals by correlation analysis. This is because when a correlation analysis is performed on a multidimensional time series signal and there are a plurality of signals having a correlation value close to 1, such that there is a high similarity, these are redundant and duplicated from the plurality of signals. This is a method of deleting signals and leaving non-overlapping ones. In addition, the user may specify. Further, in step S802, dimension reduction may be performed by various feature conversion methods such as principal component analysis, independent component analysis, non-negative matrix factorization, latent structure projection, and canonical correlation analysis.

  Next, in step S803, the learning data is divided into groups, a normal model is created in step S804, and an abnormal measure is calculated by the abnormal measure calculation unit 108 in step S805.

  As a normal model creation method, a projection distance method (PDM) or a local sub-space classifier (LSC) can be considered. The projection distance method is a method of creating a subspace having an original origin for learning data, that is, an affine subspace (space with maximum variance).

  For each cluster, an affine subspace is created as shown in FIG. FIG. 9 shows an example in which a one-dimensional affine subspace is created in a three-dimensional feature space. However, the dimension of the feature space may be larger, and the dimension of the affine subspace is also smaller than the dimension of the feature space. Any number of dimensions may be used as long as it is smaller than the number of learning data.

  A method for calculating the affine subspace will be described. First, the mean μ of the learning data and the covariance matrix Σ are obtained, then the eigenvalue problem of Σ is solved, and a matrix U in which eigenvectors corresponding to r eigenvalues designated in advance from the larger one are arranged in the affine subspace. Let it be an orthonormal basis. The abnormality measure calculated based on this normal model is defined as the minimum value of d of the projection distance to the affine subspace of each cluster. Here, it is assumed that the cluster is a collection of data of each section divided into modes as shown in FIG. 3B, for example. Alternatively, an unsupervised clustering method represented by the k-average method may be used.

  On the other hand, the local subspace method is a method of creating a k-1 dimensional affine subspace using k-neighbor data of the evaluation data q.

  FIG. 10 shows an example in the case of k = 3. As shown in FIG. 10, since the abnormal measure is expressed by the projection distance shown in the figure, the point b on the affine subspace closest to the evaluation data q may be obtained.

  In order to calculate b from the evaluation data q and its k-neighbor data xi (i = 1, ..., k), from the matrix Q and the matrix X that arranges k and q

◎

To obtain the correlation matrix C,

◎

To calculate b.

  In this method, an affine subspace cannot be created unless evaluation data is input. Therefore, in step S804 and step S809, a kd tree for efficiently searching for k-neighbor data is constructed for each mode. The kd-tree is a space division data structure that classifies points in the k-dimensional Euclidean space. The segmentation is performed using only a plane perpendicular to one of the coordinate axes, and one point is stored in each leaf node. In step S805, k-neighbor data of the evaluation data is obtained using a kd tree belonging to the same mode as the evaluation data, the point b is obtained therefrom, the distance between the evaluation data and the point b is calculated, and the anomaly measure is calculated. To do.

In addition, a normal model can be created using various methods such as Mahalanobis Taguchi method, regression analysis method, nearest neighbor method, similarity base model, and one-class SVM.

  Next, in step S808, a threshold value is set based on the abnormality measure. Specifically, the abnormal measures corresponding to the learning data calculated in S805 are sorted in ascending order, and a value that reaches a predesignated ratio is set as a threshold value. That is, when the number of data is N and the specified ratio is p, the Np-th smallest value is set as the threshold value.

  Considering that the learning unit is composed of normal data, p should be set to 1.0. In this case, the maximum value of the abnormal measure is set as a threshold value. At this time, if only the abnormal measure data for the learning data assigned the same label as the driving pattern label given to the input data to be identified for abnormality is used without using the abnormal measure for all the learning data. Good. Further, it is preferable to set the threshold value by dividing the data for each mode and performing the same process. The mode division processing may be performed by the normal model creation unit 107 by the method described above, or based on the result obtained by the operation pattern label assignment unit 104, a mode is added to the sensor signal data shown in FIG. You may keep it.

  After the normal model is created by the normal model creation unit 107, the abnormality measure calculation unit 108 calculates the abnormality measure based on the input sensor signal 102. After extracting the feature vectors by the same method as in step S802, the abnormal measure is calculated by the same method as in step S805 based on the normal model created in step S809. Next, in the abnormality identification unit 109, the abnormality measure calculated by the abnormality measure calculation unit 108 is compared with the threshold value set in step S808, and if the value is equal to or greater than the threshold value, it is detected as an abnormality.

  By using the method described above, an operation pattern label is given to the data every predetermined period, and when an abnormality is detected, a predetermined number of data of the same label or near-state label periods are collected and used as learning data. A high-accuracy normal model can be created without increasing the number, and false alarms for determining normality as abnormal can be reduced.

  Next, in the abnormality diagnosis unit 120, the sensor signal 102 and the event signal 103 from the equipment 101 are input, and the sensor signal 102 and the event signal 103 given the operation pattern label are analyzed by the sensor signal analysis unit 100. As a result, the signal output from the abnormality identification unit 109 is received, the cause event of the abnormality is estimated from the event determined to be abnormal by the abnormality identification unit 109, and the result event caused by this cause event is detected by the abnormality diagnosis unit 120. Display on a display screen (not shown). When there are a plurality of events as a result, they are displayed in order on the screen in descending order of occurrence probability. By processing the sensor signal 102 and the event signal 103 given the driving pattern label, the sensor signal analysis unit 100 can realize relatively fast processing, and the diagnosis processing in the abnormality diagnosis unit 120 is basically performed in real time. be able to.

  In the first embodiment, the method of selecting learning data based on the operation pattern label has been described on the assumption that the state of the facility changes according to the ON / OFF switching pattern, that is, the operation pattern. However, even if the operation pattern label is the same, the state may change greatly before and after maintenance work, and there is a possibility that appropriate learning data cannot be selected by the method described above.

  A second embodiment to which a function for dealing with such a case is added will be described with reference to FIG. FIG. 11 is a diagram showing the system configuration of the second embodiment for realizing the equipment state monitoring method of the present invention. The system includes a sensor signal analysis unit 1100, an abnormality diagnosis unit 1120, and an input / output unit 1130.

  The sensor signal analysis unit 1100 of the present system includes an operation pattern label assigning unit 104 that assigns an operation pattern label at regular intervals based on the sensor signal 102 and the event signal 103 output from the equipment 101, and an operation pattern label assigning unit 104. Based on the macro feature calculation unit 1101 that calculates a macro feature from the sensor signal 102 and the event signal 103 to which the label is attached, the database 1105 that accumulates the sensor signal 102 and the event signal 103 to which the operation pattern label is attached, and the operation pattern label A learning data selection unit 1106 for selecting learning data from the sensor signal 102 and the event signal 103 accumulated, a normal model generation unit 1107 for generating a normal model using the selected learning data, and an abnormal measure based on the normal model and the sensor signal. Abnormalities to calculate Degree calculation unit 1108, configured to include an abnormality identifying unit 1109 for detecting the abnormality based on the calculated anomaly measure.

  The abnormality diagnosis unit 1120 receives the sensor signal 102, the event signal 103, and the output from the abnormality identification unit 1109 and diagnoses the abnormality of the equipment 101.

  The input / output unit 1130 is connected to the driving pattern label assigning unit 104, the abnormality identifying unit 1109, the abnormality diagnosing unit 1120, etc., and inputs diagnostic conditions and outputs diagnostic results. The input / output unit 1130 includes a display screen 1131.

  The difference from the configuration of the first embodiment shown in FIG. 1 is that a macro feature calculator 1101 for calculating a feature representing a macro variation of the sensor signal 102 is added. The driving pattern label assigning unit 104 assigns the driving pattern label every predetermined period, for example, every day, and then the macro feature calculating unit 1101 cuts out the sensor signal for every same period and calculates the feature amount. As the feature amount, an average, variance, maximum value, minimum value, average of time of steady ON during the period, variance, average of time of steady OFF during the period, variance, etc. can be considered.

  FIG. 12 shows an example of the management data. This management data includes the above-described feature amounts, that is, average, variance, maximum value, and minimum value over the entire period, in the operation pattern label management data 1201 to 1209 (respectively corresponding to 501 to 509 in FIG. 5A) shown in FIG. 5A. 1210, average of time of steady ON during period, variance: 1211, average of time of steady OFF during period, variance: 1212 are added. However, it is not always necessary to add all three items, and any one or two combinations may be added.

  Next, a learning data selection processing method in the learning data selection unit 106 will be described. Basically, learning data is selected in the same way as in the first embodiment, but if there is a possibility that the status of maintenance work etc. will change significantly, learning will be performed using macro features in addition to operation pattern labels. Select data.

  Specifically, as shown in the flow chart of FIG. 13, first, the sensor signal 102 and the event signal 103 are input to the driving pattern label assigning unit 104 to the driving pattern label assigning unit 104 (S1301), and predetermined. A driving pattern label is assigned for each period, a macro feature is calculated by the macro feature calculation unit 1101, and stored in association with the driving pattern label (S1302). Next, the learning data selection unit 1106 performs learning data selection processing in the following procedure.

  First, as in the case of the first embodiment, the operation pattern label is set as a search target on the screen described with reference to FIGS. Load (S1304).

  Next, from the saved driving pattern label management data, the same driving pattern label as the driving pattern label given during the evaluation target period and data having similar macro features are searched (S1305), and the search result is recorded. (S1306). Whether or not the macro features are similar is determined based on a preset threshold value. However, data with warning is not searched. In addition, an exclusion date may be specified by a file or a user interface so that it can be removed from the search target.

  Further, it is confirmed whether or not the number of searched data has reached a predetermined number (S1307). If the number of searched data has not reached the predetermined number, the search target label is set according to the preset priority. Change and set a new target label (S1308), and return to S1305 to search for data with similar label and macro features. This is repeated until the learning data reaches a predetermined number, and sensor signal data and event signal data associated with the key code described in the searched driving pattern label management data are loaded in chronological order (S1309). For the part exceeding the predetermined number in the last search, the last search target label is selected in order from the similar feature so that the total becomes the predetermined number.

  There are several possible methods for switching whether to use the macro feature. The first method is to always select a learning pattern using macro features without switching. This method can be easily realized because the determination condition is unnecessary.

  The second method is a method of determining the presence or absence of maintenance work based on an event signal and selecting a learning pattern using a macro feature if there is a maintenance work. This method may be adopted if the event signal includes a signal indicating the start and end of maintenance work. If there is no maintenance work, it is only necessary to search from the newer data, so the search can be terminated when the predetermined number is reached. When there is a main operation pattern and there are a large number of the same labels, it is expected that the search efficiency is improved as compared with the first method.

  The third method is a method in which transition of macro features is examined to determine whether or not maintenance work is performed, and if there is maintenance work, a learning pattern is selected using the macro features. For example, if the average μ and standard deviation σ of each feature is calculated for several cycles before the evaluation target period, and there is one feature whose feature value exceeds the range of μ ± nσ, a large change in state such as maintenance work Judge that there was.

  Here, the number of periods to be examined and n are parameters. Alternatively, the distance on the feature space with the previous cycle is calculated for several cycles before the evaluation target period, and the distance on the feature space with the previous cycle of the evaluation target period is the maximum value thereof. If it is larger, it is determined that there has been a large change in state such as maintenance work.

  In addition, the center of gravity and covariance of the macro feature space may be calculated for several cycles before the evaluation target period, and the Mahalanobis distance may be used to determine the number of cycles before the evaluation target period. A one-class identification method may be used.

  Through the above processing, even when there is a large equipment state such as maintenance work, it is possible to select learning data that is close to the equipment state, and it is possible to create a normal model with high accuracy. It becomes possible to reduce false alarms due to shortage.

  However, in the above method, it is assumed that there may be a case where the number of data with the same or similar macro pattern extracted from the sensor signal with the same or similar driving pattern label is less than the predetermined number. In order to deal with this case, when the number of data with similar macro features is small, sensors after the maintenance work are excluded, and processing after learning data selection is executed.

  Whether or not it has changed due to the maintenance work is determined by calculating the average μ and standard deviation σ of each feature as described above and determining whether or not the feature value in the evaluation period exceeds the range of μ ± nσ. Alternatively, the difference from the previous period of each feature is calculated, and it is determined whether or not the difference from the previous period of the feature in the evaluation period is the maximum so far.

  In the first and second embodiments described above, it is assumed that all sensor signals and event signals output from the facility are stored with operation pattern labels. However, this method has a problem that the capacity of the database increases as time passes. In order to solve this problem, consider deleting redundant data. It is determined whether or not the deletion target is the same label and exceeds a preset number larger than the number selected as learning data, and whether or not the deletion can be performed in units of operation pattern label management data.

  Basically, even if it is excluded, data that has little influence is deleted. That is, it is deleted after confirming that there is other similar data or that the result does not change even if it is actually removed.

  For example, the one having the smallest distance to the second nearest data among the smallest distances to the nearest data on the feature space of the macro feature is deleted. Alternatively, clustering is performed using macro features using the data of the same label, and the oldest date is deleted from the clusters with many belonging data. Alternatively, the abnormal measure is calculated by cross-validation using all the data of the same label as the learning data except for the evaluation target data, and the data in the period in which the maximum value of the abnormal measure is minimum is deleted.

  Alternatively, the abnormal measure calculation by the cross validation may be performed by excluding one period from the data with the same label, and the data in the exclusion period with the smallest change in the abnormal measure calculation result may be deleted.

  When the deletion target is determined, the sensor signal data and event signal data associated with the management data are completely deleted from the database. The management data is not deleted, and a numerical value indicating that the data has been deleted, for example, 0 is entered in the key code column.

  According to the above method, it is possible to create a normal model with high accuracy without increasing the calculation load and also to reduce the database capacity.

  The present invention includes a power supply facility such as a gas turbine, a facility for supplying hot water for district heating using waste heat of the gas turbine, a facility for supplying high-pressure steam and low-pressure steam to a factory, a water turbine in a hydroelectric power plant, Early detection of abnormalities based on multi-dimensional time-series data output from nuclear power plant reactors, wind turbines, wind turbines, aircraft and heavy machinery engines, railway vehicles, tracks, escalators, elevators, and other plants and equipment It can be used for preventive maintenance.

DESCRIPTION OF SYMBOLS 100, 1100 ... Sensor signal analysis part 101 ... Equipment 102 ... Sensor signal 103 ... Event signal 104 ... Operation pattern label provision part 105, 1105 ... Database 106, 1106 ... Learning data selection part 107, 1107 ... Normal model creation part 108, 1108 ... Abnormality measure calculation unit 109, 1109 ... Abnormality identification unit 120, 1120 ... Abnormality diagnosis unit

Claims (15)

  A method for monitoring the state of equipment based on a time series signal output from the equipment, wherein an operation pattern label is given every fixed period based on the time series signal, and based on the operation pattern label for each fixed period Select learning data, create a normal model based on the selected learning data, calculate an abnormal measure based on the time series signal and the normal model, and the equipment state is abnormal or normal based on the calculated abnormal measure A facility state monitoring method characterized by identifying whether or not.   As the operation pattern label, different operation pattern labels are given to the equipment in a steady-off state, the equipment in a steady-on state, the equipment start-up operation state, and the equipment stop-operation state. The equipment state monitoring method according to claim 1.   The facility state monitoring method according to claim 1, wherein the normal model is created for each operation pattern label.   A method of monitoring the state of equipment based on a time-series signal output from the equipment, the operation pattern label attaching step for giving a finite number of operation pattern labels categorized at a fixed period to the time-series signal, A data accumulation step for accumulating the time series signal to which the operation pattern label is given by the operation pattern label assignment step as data, and the operation pattern label given to the time series signal from the data accumulated in the data accumulation step A learning data selection step for selecting a predetermined number of data based on the learning data, a normal model creation step for creating a normal model using the learning data selected in the learning data selection step, and a normal model creation step. An anomaly measure calculating step for calculating an anomaly measure of the time series signal based on comparison with the created normal model, and the anomaly measure calculation Abnormality recognizing step and equipment condition monitoring method characterized by comprising identifying the abnormal based on the calculated anomaly measure in step.   In the learning data selection step, learning data is selected by selecting, from the accumulated data, a predetermined number of data of driving pattern labels that are the same as driving pattern labels assigned to the time-series signal or driving pattern labels that are in a similar state. The equipment state monitoring method according to claim 4, wherein:   The operation pattern label assigning step includes a plurality of operation pattern features including a steady-off state of the equipment, a steady-on state of the equipment, a start-up state of the equipment, and a stop-operation state of the equipment every predetermined period. The facility state monitoring method according to claim 4, further comprising: a step of calculating an operation pattern, and a label applying step of applying an operation pattern label based on a combination of the plurality of operation pattern features.   The method further comprises a macro feature calculating step for calculating a feature representing a macro fluctuation for each predetermined period of the time series signal, and in the learning data selection step, the driving pattern label given to the time series signal and the calculated macro 5. The equipment state monitoring method according to claim 4, wherein a predetermined number of data is selected from the accumulated data based on characteristics and used as learning data.   In the learning data selection step, from the accumulated data, the operation pattern label that is the same as the operation pattern label given to the time series signal or the operation pattern label that is in a similar state and the macro features related to the time series signal are 8. The facility state monitoring method according to claim 7, wherein a predetermined number of similar data is selected as learning data.   In the learning data selection step, the presence / absence of a significant state change is determined based on the time series signal, and when there is no significant state change, the time series signal is added to the time series signal from the accumulated data. A predetermined number of data of the operation pattern label that is the same as the operation pattern label or an operation pattern label that is in a similar state is selected as learning data, and when there is a significant state change, the time series is selected from the accumulated data. A driving pattern label that is the same as the driving pattern label given to the signal or a driving pattern label that is in a similar state and a predetermined number of data having similar macro characteristics related to the time-series signal are selected as learning data. The equipment state monitoring method according to claim 7.   The macro feature is an average, variance, maximum value, minimum value, average of time of steady-on during the period, variance or average of time of steady-off during the period, variance over the entire period of the sensor signal. The equipment state monitoring method according to claim 7, comprising at least one of them.   The facility state monitoring method according to claim 9, wherein the presence / absence of the noticeable state change is determined based on a change in the macro feature. A sensor signal analysis unit that inputs and analyzes a time-series signal output from the facility; An apparatus that monitors an equipment state including an input / output unit that inputs and outputs data by connecting to the sensor signal analysis unit and the abnormality diagnosis unit,
The sensor signal analysis unit includes an operation pattern label attaching unit that assigns an operation pattern label every predetermined period based on a time-series signal output from the facility, and a fixed period of time provided by the operation pattern label attaching unit. Learning data creation means for selecting learning data based on the driving pattern label, normal model creation means for creating a normal model based on the learning data created by the learning data creation means, and normal created by the normal model creation means An anomaly measure calculating means for calculating an anomaly measure of the time series signal output from the equipment based on the model, and identifying whether the equipment state is anomalous or normal based on the anomaly measure calculated by the anomaly measure calculating means An equipment state monitoring device comprising an abnormality identification means.   It further comprises database means for storing the time series signal to which the driving pattern label is given by the driving pattern label giving section, and the learning data creation means is the driving pattern given to the time series signal by the driving pattern label giving means. 13. The equipment state according to claim 12, wherein a predetermined number of data of the same operation pattern label or operation pattern labels having similar states are selected from the data stored in the database means based on the label, and used as learning data. Monitoring device.   An apparatus for monitoring a facility state by inputting a time series signal output from the equipment, wherein the operation pattern label is categorized into a finite number of fixed time series signals by inputting the time series signal and externally inputting the time series signal. The operation pattern label attaching means for providing the data, the data accumulating means for accumulating the time series signal given the operation pattern label by the operation pattern label attaching means, and the time series signal given by the operation pattern label attaching means Learning data selecting means for selecting learning data by selecting a predetermined number of data of the same driving pattern label or driving pattern labels in a similar state from the data accumulated in the data accumulating means based on the label, and learning data selection A normal model creating means for creating a normal model using the learning data selected by the means, and the operation pattern An anomaly measure calculating means for calculating an anomaly measure of the time series signal by comparing the time series signal given the driving pattern label by the bell attaching means with the normal model created by the normal model creating means, and the anomaly measure calculating means An equipment state monitoring device comprising: an abnormality identifying means for identifying an abnormality of the time-series signal to which the operation pattern label is assigned based on the abnormality measure calculated in (1). 15. The equipment state monitoring apparatus according to claim 14, further comprising an input unit for inputting a condition for applying the operation pattern label to the operation pattern label applying unit.

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