JP2013041491A - Abnormality diagnostic device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To diagnose abnormality in a plant with high accuracy.SOLUTION: An abnormality diagnostic device comprises: a calculation unit 13 for reading out, at a predetermined timing, group data associated with a plurality of groups composed of a plurality of different variables from a group data storage unit 20 and calculating, for each group, a Mahalanobis distance by using values of variables contained in the read-out group data and a plurality of values of variables presenting a plant state input from the plant; a diagnosis unit 14 for diagnosing abnormality for each group by comparing the Mahalanobis distance calculated by the calculation unit 13 and a determination value that is predetermined for each group and is used for determining abnormality; and a determination unit 15 for determining a cause of abnormality by integrating diagnostic results for all the groups in the diagnosis unit 14.

Description

  The present invention relates to an abnormality diagnosis device for diagnosing plant abnormality.

  In the plant, a plurality of devices are operated under various conditions. In the plant, the state is measured using a plurality of sensors. An abnormality of the plant can be detected from the plurality of condition values and the measurement values of a plurality of sensors. The method of judging an abnormality when exceeding the mainstream is the mainstream. On the other hand, when the upper and lower limit values are set for each measured value of each sensor, it is necessary to increase the number of upper and lower limit values when the number of sensors used is increased, and there is a problem in that the management of values for abnormality determination becomes complicated.

  On the other hand, in recent years, there is also a technique for diagnosing an abnormality using a pattern recognition technique called MT method (Mahalanobis Taguchi method). The MT method is for diagnosing whether or not the state is “same as usual”, and the Mahalanobis distance of the object to be diagnosed is determined from the Mahalanobis space based on the center of the Mahalanobis space that represents the same state as usual. This is a method for determining an abnormality when going far. Specifically, there is one that divides sensor measurement data into a plurality of groups, generates a unit space for each group, and diagnoses an abnormality using the MT method (see, for example, Patent Document 1).

  On the other hand, when the MT method is used, the determination value (threshold value) used for determining abnormality is generally about 4 for any unit space and for any state. . However, the Mahalanobis distance actually required varies greatly depending on the target unit space, and is wide from one digit to several digits. Therefore, when the determination is made without using the optimum threshold, it is difficult to diagnose an abnormality with high accuracy.

JP 2010-181188 A

  As described above, it is difficult to diagnose an abnormality with high accuracy in the conventional abnormality diagnosis.

  In view of the above problems, an object of the present invention is to provide an abnormality diagnosis apparatus that performs an abnormality diagnosis with high accuracy.

  In order to achieve the above object, the invention according to claim 1 reads out group data relating to a plurality of groups composed of a plurality of different variables from the group data storage unit at a predetermined timing, and reads out the group for each group The calculation unit that calculates the Mahalanobis distance using the value of the variable included in the data and the value of a plurality of variables that represent the state of the plant newly input from the plant, and the calculation unit calculated for each group Comparing the Mahalanobis distance with the diagnosis value that is used to determine the abnormality set in advance for each group and diagnosing the abnormality, and integrating the diagnosis results for all groups in the diagnosis part A specifying unit for specifying.

  The invention of claim 2 further includes a changing unit that changes the determination value of the group identified as abnormal at a predetermined timing.

  According to the present invention, it is possible to improve the accuracy of plant abnormality diagnosis.

It is a block diagram explaining the abnormality diagnosis apparatus concerning an embodiment. It is a data block diagram explaining an example of the accumulation | storage data utilized with an abnormality diagnosis apparatus. It is a data block diagram explaining an example of the group data utilized with an abnormality diagnosis apparatus. It is a flowchart explaining an example of the process in an abnormality diagnosis apparatus. It is a conceptual diagram explaining the abnormality diagnosis using MT method.

  The abnormality diagnosis apparatus according to the embodiment is an abnormality diagnosis apparatus that diagnoses an abnormality in plant operation. For example, the plant diagnosed by the abnormality diagnosis device is a power plant. The power plant includes a plurality of devices (pumps, valves, etc.), and values for controlling these devices are set as target values. This target value is, for example, the pressure of the pump, the opening / closing of a valve, or the like. In addition, the power plant includes a plurality of sensors, and each sensor measures the intake air amount, the exhaust air amount, the power generation amount, and the like.

  Furthermore, in the power plant, various operating states can be set, and the target value set for each device and the measured value measured by each sensor differ depending on the operating state. For example, in a power plant, it is not always necessary to generate a certain amount of power, and power is generated in accordance with power demand. Therefore, the operation state can be changed according to the time zone, day of the week, season, etc., and the target value of each device varies depending on the operation state. In addition, since the target value of each device is different, the measured value of each sensor is different in the operating state.

  As shown in FIG. 1, the abnormality diagnosis apparatus 1 according to the embodiment generates an update unit 11 that updates accumulated data 21 with new variables input from a plant, and groups used for abnormality diagnosis. A generation unit 12 that sets a determination value used in diagnosis and stores it as group data 22, and a calculation unit 13 that calculates Mahalanobis distances for a plurality of groups using a variable newly input from the plant and a variable of group data 22. And a diagnosis unit 14 for diagnosing an abnormality for each group using the Mahalanobis distance calculated by the calculation unit 13, and a cause of the abnormality from the specific data 23 using a diagnosis result regarding each group obtained by the diagnosis unit 14. The specifying unit 15 for specifying and the changing unit 16 for changing the determination value of the group data 22 are provided.

  The abnormality diagnosis apparatus 1 is, for example, an information processing apparatus including a central processing unit (CPU) 10 and a storage device 20, and the abnormality diagnosis program P stored in the storage device 20 is read and executed. As illustrated in FIG. 1, an update unit 11, a generation unit 12, a calculation unit 13, a diagnosis unit 14, a specification unit 15, and a change unit 16 are mounted on the CPU 10. In addition to the abnormality diagnosis program P, the storage device 20 stores accumulated data 21, group data 22, and specific data 23. The abnormality diagnosis device 1 is connected to an input device 2 such as a keyboard, a mouse, an operation button, and a touch panel for inputting operations, and an output device 3 such as a display and a speaker for outputting an abnormality diagnosis process and results. It is connected.

  When the value of each variable is newly input from the plant, the update unit 11 generates a new record including the value of each input variable, adds the generated record, and stores the accumulated data 21 stored in the storage device 20. Update.

  The accumulated data 21 is data obtained by accumulating variable records representing past plant states. The variables of the accumulated data 21 are the plant operation condition values and the measured values measured in the plant in the case of the condition values. In the example shown in FIG. 2, the accumulated data 21 includes a target value (variables 1 and 2) set in each device of the plant, which is a condition value for plant operation, and a measured value measured in the case of this mode and the target value. (Variables 4, 5) and the like are associated with the record. In the example of the accumulated data 21 shown in FIG. 2, each record is given a sample number such as time.

  The generation unit 12 reads the accumulated data 21 from the storage device 20 at a predetermined timing for generating a group, extracts records in which variables satisfy a predetermined extraction condition, and generates a plurality of groups. This extraction condition is input via the input device 2, for example. For example, any one of the values of variable 1 and variable 2 (target value), variable 4 and variable 5 (measured value) may be used as the extraction condition, or a combination of values of a plurality of variables may be used as the extraction condition. good. The generation unit 12 sets determination values used for plant abnormality diagnosis in the plurality of generated groups, generates group data 22 including the values and determination values of the variables in the group, and stores them in the storage device 20.

  That is, the group data 22 is data having only the selected record of the accumulated data 21 as shown in FIG. 3 (a) and FIG. 3 (b). In the example of FIG. 1, the storage device 20 is illustrated as storing one group data 22, but the storage device 20 stores a plurality of group data 22, that is, group data 22 for each group. It shall be.

  Here, the predetermined timing at which the generation unit 12 generates a group is, for example, a periodic timing or a timing input via the input device 2. Moreover, the value used as the determination value by the generation unit 12 is a value representing the Mahalanobis space obtained for this group. This determination value is input via the input device 2, for example.

  The calculation unit 13 reads the group data 22 of each group from the storage device 20 at a predetermined timing for diagnosing an abnormality, and extracts the value of each variable included in the read group data 22. Further, the calculation unit 13 obtains the Mahalanobis distance of each group using the value of each variable extracted from the group data 22 and the new variable input from the plant, and outputs the obtained value to the diagnosis unit 14. Here, the predetermined timing for diagnosing abnormality is, for example, a regular timing.

  Specifically, (1) First, the calculation unit 13 obtains an average value and a standard deviation for each variable. (2) Thereafter, the calculation unit 13 standardizes the data using the value of each variable and the average value and standard deviation obtained for each variable, and obtains a normalized value for each variable. (3) Subsequently, the calculation unit 13 obtains a correlation matrix for each variable by using the normalized value obtained for each variable, and obtains an inverse matrix of the correlation matrix. (4) Finally, the calculation unit 13 obtains the Mahalanobis distance using the obtained inverse matrix. Based on the Mahalanobis distance determined in this way, it is possible to identify the difference between the current state and the normal state for the value of the target group, and to determine the difference between the current state and the normal state in the plant. it can.

  When the diagnosis unit 14 inputs the Mahalanobis distance of each group from the calculation unit 13, the diagnosis unit 14 reads the group data 22 of each group, and extracts the determination value of the read group data 22. Further, the diagnosis unit 14 compares the Mahalanobis distance input from the calculation unit 13 with the determination value extracted from the group data 22 for each group, and determines whether the operation state of the plant is normal or abnormal, It outputs to the specific | specification part 15 and the output device 3 as a diagnostic result with Mahalanobis distance of each group. For example, the output device 3 that inputs a diagnosis result from the diagnosis unit 14 can output a diagnosis result by the diagnosis unit 14 and notify the user or the like of the occurrence of an abnormality.

  For example, as illustrated in FIG. 5, when determining an abnormality using the MT method, when the Mahalanobis distance obtained by the calculation unit 13 is smaller than a set determination value, that is, within the Mahalanobis space, the diagnosis unit 14 Diagnoses that the plant is operating normally, assuming that the current plant state is the same as the normal state. On the other hand, when the Mahalanobis distance obtained by the calculation unit 13 is larger than the set determination value, that is, when the Mahalanobis distance deviates from the Mahalanobis space, the diagnosis unit 14 is in a state where the current plant state is different from the usual state. And diagnose that an abnormality has occurred in the plant.

  When inputting the diagnosis result from the diagnosis unit 14, the specifying unit 15 reads the specific data 23, uses the diagnosis result regarding each group of the diagnosis unit 14, specifies the cause of the abnormality, and changes the specified cause of the abnormality 16 and the output device 3. For example, the output device 3 that inputs the cause of the abnormality from the specifying unit 15 outputs the cause of the abnormality specified by the specifying unit 15 and notifies the cause of the abnormality.

  The specific data 23 is data in which the diagnosis result of each group is associated with the abnormality cause pattern. Here, for example, in the specific data 23, the change pattern of the Mahalanobis distance of each group may be associated with the cause of abnormality. That is, when the Mahalanobis distance for a certain group is increasing, it is possible to specify that a predetermined part of the plant is the cause of the abnormality. When the cause of the abnormality is specified from the Mahalanobis distance change pattern in this way, the abnormality diagnosis apparatus 1 stores the history of the Mahalanobis distance change in the storage device 20 or the like, and the specifying unit 15 newly inputs the diagnosis. In addition to the result, the cause of the abnormality is specified using the history of the Mahalanobis distance stored for a predetermined period.

  The changing unit 16 obtains a new determination value at the timing of updating the determination value used for abnormality diagnosis, and changes the determination value of the group data 22. The timing at which the determination value used for abnormality diagnosis is updated is, for example, the timing at which the cause of the abnormality is identified by the identifying unit 15. Alternatively, the changing unit 16 may update the determination value at regular timing.

  For example, the changing unit 16 obtains a new determination value for the target group from the statistical value obtained by analyzing the average value of the Mahalanobis distance in each past state. Specifically, by comparing the average value of the Mahalanobis distance at the normal time and the average value of the Mahalanobis distance at the time of the abnormality, a determination value used for determining the abnormality can be obtained.

  In addition, the abnormality diagnosis apparatus 1 may be comprised from several information processing apparatus, for example, only the update part 11 may be contained in the information processing apparatus different from the other process parts 12-16. Further, only a part of the data stored in the storage device 20 may be stored in an external storage device.

  Next, processing for diagnosing an abnormality in an optimum group in the abnormality diagnosis apparatus 1 will be described using the flowchart shown in FIG.

  The generation unit 12 reads the accumulated data 21 from the storage device 20 at the timing when the group data 22 is generated, extracts a record value satisfying the extraction condition, generates the group data 22 and stores the group data 22 in the storage device 20 (S1). ).

  Thereafter, the calculation unit 13 reads the group data 22 from the storage device 20 for each group, and calculates the Mahalanobis distance by using the value of the variable input from the plant and the value of the variable included in the group data 22 ( S2).

  The diagnosis unit 14 that has input the Mahalanobis distances of a plurality of groups from the calculation unit 13 compares the input Mahalanobis distances with the determination values set in the group data 22 for each group, and whether the operation state of the plant is abnormal. Diagnosis is performed, and the diagnosis result is output to the output device 3 and the specifying unit 15 (S3).

  The identifying unit 15 integrates the diagnosis results of each group, and when there is an abnormality, identifies the abnormal part using the specific data (S4).

  In the abnormality diagnosis apparatus 1, when it is not the timing for changing the determination value (NO in S5), the processes in steps S2 to S4 are repeated. Moreover, in the abnormality diagnosis apparatus 1, when the determination value change timing is reached (YES in S5), the determination value is changed by the changing unit 16 (S6), and then the processes in steps S2 to S4 are repeated.

  Note that the flowchart shown in FIG. 4 does not show that the generation of the group data 22 is repeated, but the group data 22 may also be generated repeatedly at a predetermined timing such as periodically.

  As described above, in the abnormality diagnosis device according to the present invention, after diagnosing an abnormality based on the diagnosis criteria (determination values) set for each group, the diagnosis results of a plurality of groups are integrated to diagnose the state of the plant. Therefore, it is possible to diagnose an abnormality with high accuracy based on the optimum judgment standard. Further, since the abnormality diagnosis apparatus diagnoses a plant abnormality using a judgment standard (judgment value) changed while performing abnormality diagnosis, the abnormality diagnosis can be performed with high accuracy.

  As mentioned above, although this invention was demonstrated in detail using embodiment, this invention is not limited to embodiment described in this specification. The scope of the present invention is determined by the description of the claims and the scope equivalent to the description of the claims.

1 ... Abnormality diagnosis device 10 ... CPU
DESCRIPTION OF SYMBOLS 11 ... Update part 12 ... Generation part 13 ... Calculation part 14 ... Diagnosis part 15 ... Identification part 16 ... Change part 20 ... Storage device 21 ... Accumulated data 22 ... Group data 23 ... Specific data P ... Abnormality diagnosis program 2 ... Input device 3 ... Output device

Claims (2)

An abnormality diagnosis device for diagnosing plant abnormality,
At a predetermined timing, group data relating to a plurality of groups constituted by a plurality of different variables is read from the group data storage unit, and for each group, the value of the variable included in the read group data and the newly input from the plant A calculation unit for calculating the Mahalanobis distance using the values of a plurality of variables representing the state of the plant;
For each group, a diagnosis unit for diagnosing an abnormality by comparing the Mahalanobis distance calculated by the calculation unit and a determination value used for determination of abnormality set in advance for each group,
Integrating the diagnostic results for all groups in the diagnostic unit, identifying the cause of the abnormality,
An abnormality diagnosis apparatus comprising:   The abnormality diagnosis apparatus according to claim 1, further comprising a changing unit that changes a determination value of a group identified as abnormal at a predetermined timing.

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