JPH02123402A - Diagnostic device for abnormality of plant - Google Patents

Abstract

PURPOSE:To reduce personal labor by providing the title device with a diagnostic knowledge leading-out mechanism and inputting a system diagram for an object to be diagnosed, observing points and a diagnostic knowledge forming out-line to form diagnostic knowledge. CONSTITUTION:The diagnostic knowledge leading-out mechanism 120 forms diagnostic knowledge based upon the plant system diagram, the observing points and the diagnostic knowledge forming list and stores the formed diagnostic knowledge in a diagnostic knowledge file 101 in an abnormality diagnostic device 110. An observed value is inputted to an observed value input means 102 and an observed result is diagnosed by a diagnostic processing means 103 in accordance with the diagnostic knowledge outputted from the file 101. The cause of abnormality is displayed on a diagnostic result display device 104. The diagnostic knowledge formed by an introducing mechanism 120 has a function for forming a matrix among the abnormality of a plant, a causing equipment and an observing signal. Since a diagnostic matrix formed by manual operation in an ordinary method can be formed by the mechanism 120, personal labor can be reduced.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an abnormality diagnosis device and an operation support device used in the operation of a plant or the like.

INDUSTRIAL APPLICATION This invention can be utilized for abnormality diagnosis or operation support of a thermal power plant, a chemical plant, and various other plants.

[Prior Art] (Abnormality Diagnosis Device) FIG. 14 shows a conventional example of an abnormality diagnosis device. In FIG. 14, 100 indicates the entire abnormality diagnosis device.

A diagnostic knowledge file 101 stores diagnostic knowledge.

102 is an observed value input means for inputting observed values.

103 is a diagnostic processing means that combines diagnostic knowledge from 101 and 102;
Diagnostic results are derived from the observed values.

A diagnostic result display 104 displays the cause of the abnormality based on the diagnostic results from 103.

(Driving Support Device) FIG. 15 shows a conventional example of a driving support device. In FIG. 15, 200 indicates the entire driving support device.

A diagnostic knowledge file 201 stores diagnostic knowledge.

202 is an observed value input means for inputting observed values.

203 is a diagnostic processing means, which combines the diagnostic knowledge from 201 and 20
Diagnosis results are derived from the observed values from 2.

Reference numeral 204 is an operational knowledge file that stores operational knowledge.

205 is an operation guide processing means, which collects the observed value of 202 and 20
An operation guide is derived from the diagnosis result of 3 and the operation knowledge of 204. A display of the diagnosis result and operation guide 206 displays the cause of the abnormality based on the diagnosis result of 203 and the operation guide of 205.

[Problems to be Solved by the Invention] (Abnormality Diagnosis Device) Conventionally, it took many people's effort to input diagnostic knowledge into an abnormality diagnosis device.

In other words, a lot of manual labor is required to create diagnostic knowledge using plant system diagrams and other knowledge, and to convert the diagnostic knowledge into an expression format suitable for the diagnostic knowledge file inside the abnormality diagnosis device.

(Driving support equipment) Conventionally, operating knowledge is created using plant system diagrams and other knowledge, and then the operating knowledge is converted into an expression format suitable for the operating knowledge file inside the driving support equipment, which requires a lot of manual labor. It was through.

An object of the present invention is to provide a device that reduces these manual efforts.

[Means for Solving the Problems] (1) The plant abnormality diagnosis device according to the present invention includes a diagnostic knowledge file for storing diagnostic knowledge, an observed value input means for inputting observed values, and the diagnostic knowledge and the observed values. In a plant abnormality diagnosis device consisting of a diagnostic processing means that derives a diagnosis result from the above, and a display that displays the cause of the abnormality based on the diagnosis result, the plant can be diagnosed by inputting the plant system diagram, observation points, and diagnostic knowledge creation guidelines. It is characterized by having a diagnostic knowledge derivation mechanism that creates a matrix between abnormalities, causative devices, and observed signals.

(2) The plant operation support device according to the present invention includes a diagnostic knowledge file that stores diagnostic knowledge, an observed value input device that inputs observed values, and a diagnostic processing device that derives diagnostic results from the diagnostic knowledge and observed values. , an operation knowledge file for storing operation knowledge, the observed value, the diagnosis result, an operation guide processing means for guiding an operation guide from the operation knowledge, and a display of the diagnosis result and the operation kite. The present invention is characterized by having an operating knowledge deriving mechanism that can submit operable equipment and its operating guide when a plant equipment malfunctions by inputting a plant system diagram and operating knowledge creation procedure.

[Function] (1) The diagnostic knowledge derivation mechanism uses the system diagram of the diagnosis target, observation points,
To create diagnostic knowledge by inputting a diagnostic knowledge creation procedure, and to convert the knowledge into diagnostic knowledge in an expression format inside a diagnostic device without requiring manual labor.

(2) The operation knowledge derivation mechanism creates operation knowledge by inputting the plant system diagram for operation support and the operation knowledge creation procedure, and also converts the knowledge into an expression format within the operation support device without requiring human intervention. Convert.

[Example] Embodiments of the present invention are shown in FIGS. 1 to 13.

FIG. 1 is an embodiment of claim 1, and shows an overall configuration diagram of a plant abnormality diagnosis device.

In Figure 1 110 is an abnormality diagnosis device of the present invention.

Reference numeral 120 denotes a diagnostic knowledge deriving mechanism, which generates diagnostic knowledge by inputting a system diagram of a diagnosis target, observation points, and a diagnostic knowledge creation procedure.

101 to 104 are the same as in the conventional example (FIG. 14).

FIG. 2 shows the processing flow of the diagnostic knowledge derivation mechanism 120 of FIG. 1.

FIG. 3 shows an example of the hardware configuration for FIGS. 1-2.

In Figure 3 11 is a CPU that performs various processing and controls thereof.

Reference numeral 12 denotes an observation value input device consisting of an insulation amplifier, a filter, and the like.

13 is an A/D converter which converts the analog value into a digital value for input to the CPU.

Reference numeral 14 is a diagnostic result display that displays the cause of the abnormality.

15 is an external memory that stores diagnostic knowledge, observed values, and diagnostic results.

16 is a data input device for deriving diagnostic knowledge, which can input system diagrams, observed values, and diagnostic knowledge creation guidelines, and can also be edited.
It is an input device including RT.

In FIG. 3, data is input using a data input device 16 for deriving diagnostic knowledge. A touch panel or keyboard is used to input the system diagram, and the equipment (name, model formula, numerical values used, etc.) and its connections are selected from registered items to create the system diagram.

Observation points are entered by pointing out the above equipment on the system diagram (diagram created from the above input). At the same time, input whether the signal is an analog signal or a digital signal.

The diagnostic knowledge creation procedure is first input by pointing out system-level abnormality detection signals on the system diagram. At the same time, input the threshold value (the value that distinguishes between normal and abnormal function values of the function generator).

Next, the equipment level abnormality detection signal is pointed out on the system diagram and input. Enter the threshold value at the same time.

The input data is stored in the memory of the input device. Creation of system-level and equipment-level diagnostic diagrams and matrices
After the data input is completed, it is performed using a program stored in the input device. The creation result is stored in the memory of the input device.

The output of the creation results, that is, the created system diagram, diagnostic diagram, and matrix are displayed on the CRT of the data input device for deriving diagnostic knowledge.

Further, the diagnostic diagram and the abnormality detection signal matrix are output to the CPU 11 and stored in the external memory 15 as a diagnostic knowledge file.

Editing of input data (new input, modification, storage, etc.) is performed using the input device 16. This result can be replaced with a previously created diagnostic knowledge file via the CPU.

FIG. 4 is an example of a system level diagnosis diagram in which the pressurizer pressure control system of a PWR plant is the system to be diagnosed.

S-1 to S-9 in the figure are abnormality detection signals.

FIG. 5 shows a signal generation method using the abnormality detection signals S-1 to S-3 as examples. That is, the actual measured value and the reference value (which may be a calculation result based on a physical model of the target device that is regarded as a normal value) are compared, and if it exceeds a predetermined threshold value, it is detected as abnormal.

FIG. 6 is a matrix of system-level abnormality detection signals and actually measured value signals used therein, and is indicated by a circle. A blank field means not used.

FIG. 7 is an example of a diagnostic diagram at the equipment level. At the system level, when deriving reference value 1, processing is performed in a closed loop as a pressurizer pressure control system. Therefore, the input data when calculating the system is limited to signals connected from outside the closed loop, and the calculation results inside the closed loop are used for the rest. On the other hand, at the equipment level, all inputs of each equipment model are regarded as an open loop with actual measured values, the reference value 2 is calculated, and the result is compared with the actual measured value to detect an abnormality in the equipment. The abnormality detection signal is coded C-1.
- Shown as C-3.

FIG. 8 shows an example of an equipment level abnormality detection signal.

In FIG. 9, equipment-level abnormality detection signals, actual measurement value signals, and elemental equipment are indicated by ○ marks.

To diagnose whether or not there is an abnormality, use s- or c-j after detecting an abnormal signal.
By detecting (i-1~, j-1~), the extracted signal and element equipment with the Q mark are considered as cause candidates, and s-i,
This is done by distinguishing whether there is an abnormality in the output signal (cable, measuring device, etc. for extracting the actual measured value) or an abnormality in the element equipment, based on the combination of c-j detections.

FIG. 10 is an embodiment of claim 2, and shows an overall configuration diagram of a plant operation support device.

In Figure 10 210 is a driving support device of the present invention.

Reference numeral 220 denotes an operation knowledge derivation mechanism, which generates operation knowledge when a plant system diagram to be supported for operation and operation knowledge creation guidelines are input.

201 to 206 are similar to the conventional example (FIG. 15).

FIG. 11 shows the processing flow of the operation knowledge derivation mechanism 220 shown in FIG. 10.

The hardware configuration example for Figures 10 and 11 is shown in the third example.
As shown in the figure.

In FIG. 3, data input to the driving support system is performed using a data input device 16 for deriving diagnostic knowledge, and a plant system diagram and operation knowledge creation procedure are input. The system diagram is input using a touch panel or keyboard, and the system diagram input by the abnormality diagnosis device is used.

The equipment to be diagnosed in the operating knowledge creation guideline is the equipment input by the abnormality diagnosis device. Then, point out the position of the important process signal on the system diagram, and input the name and upper/lower limit value. The component devices are retrieved using a program stored in the input device. Here, the equipment to be diagnosed is extracted from the system diagram.

The matrix is created using all the combinations of the devices extracted above, their operating states, and the states (normal/abnormal) of the limiting conditions of the important process signals. The operable devices and guides are extracted by extracting the operable devices from the diagnosis target devices and abnormal operating conditions in the matrix above and entering them in the matrix, and by creating the operation guide from the limit conditions of the operable devices and process signals. conduct.

Both the output of the creation matrix and the editing of input data are possible using the CRT of the input device. The creation matrix including input data is stored on the input device side.

The creation matrix can be stored in an external memory via the CPU and replaced with a previously created operation knowledge file.

FIG. 12 is an example of creating operational knowledge regarding the system targeted for driving support in FIG. 13.

FIG. 13 shows a system diagram consisting of two pumps, four manual control valves, and two tanks, in which the control valves are operated to maintain the water level of each tank at a constant value within 20 to 60%.

In FIG. 12, in the column of diagnosis results, all combinations of diagnosis of the component devices of the support target system (0: normal, 1: abnormal) are entered. The operating status column is pump A.

The usage status of B (0: stopped, 1: running) is shown, and the process status column shows whether the water level of tanks A and B, which are important for operation, exceeds the limit water level or is within the limit (0: within the limit, 1: (over the limit), the usable device column shows the name of the currently operable device. In the operation guide column, enter the operation method corresponding to the situations in the four columns.

[Effects of the Invention] Since the present invention is configured as described above, it produces the effects described below.

(Abnormality Diagnosis Device) With the diagnostic knowledge deriving mechanism, it is possible to create diagnostic knowledge, that is, a diagnostic matrix, which was conventionally created manually, without having to obtain it, thereby reducing human effort. Therefore, an efficient abnormality diagnosis device can be provided.

(Driving support device) The operating knowledge derivation mechanism allows operating knowledge that was previously created manually to be created without having to obtain it. Therefore, it is possible to provide a driving support device with high development efficiency.

[Brief explanation of the drawings] Fig. 1 is an overall configuration diagram of the abnormality diagnosis device according to the present invention, and Fig. 2
The figure is a flowchart of the diagnostic knowledge derivation mechanism, Figure 3 is a diagram showing examples of the hardware configuration of Figures 1 to 2 and Figures 10 to 11, and Figure 4 is an example of system level diagnosis. Figure 5 is a diagram showing an example of a system level abnormality detection signal, Figure 6 is a diagram showing an example of a matrix of system level abnormality detection signals and extraction signals, and Figure 7 is an example of equipment level diagnosis. Figure 8 is a diagram showing an example of an abnormality detection signal at the equipment level, Figure 9 is a diagram showing an example of an equipment level abnormality detection signal and extraction signal, and a matrix of elemental equipment, and Figure 10 is a diagram showing an example of an equipment level abnormality detection signal. An overall configuration diagram of a driving support device according to an embodiment of the invention, FIG. 11 is a diagram showing the processing flow of the operation knowledge derivation mechanism of the invention, and FIG. FIG. 13 is a system diagram of the support target for which the operation knowledge of FIG. 12 is created, FIG. 14 is a diagram showing a conventional abnormality diagnosis device, and FIG. 15 is a diagram showing a conventional driving support device. 110... Abnormality diagnosis device, 120... Diagnostic knowledge deriving mechanism, 210... Driving support device, 220... Operation knowledge deriving mechanism, Applicant's representative Patent attorney Takehiko Suzue Figure 15

Claims (2)

[Claims] (1) A diagnostic knowledge file that stores diagnostic knowledge, an observed value input device that inputs observed values, a diagnostic processing device that derives diagnostic results from the diagnostic knowledge and the observed values, and displays the cause of the abnormality based on the diagnostic results. In a plant abnormality diagnosis device consisting of a display device, a diagnostic knowledge derivation mechanism is developed that creates a matrix between plant abnormalities, causative devices, and observation signals by inputting plant system diagrams, observation points, and diagnostic knowledge creation guidelines. A plant abnormality diagnosis device comprising: (2) A diagnostic knowledge file that stores diagnostic knowledge, an observed value input device that inputs observed values, a diagnostic processing device that derives diagnostic results from the diagnostic knowledge and observed values, and an operational knowledge file that stores operational knowledge. , an operation guide processing means for guiding an operation guide from the observed values, the diagnosis results, and the operation knowledge, and a display for the diagnosis results and operation guide, the plant operation support device comprising: a plant system diagram and operation knowledge creation; A plant operation support device characterized by having an operation knowledge derivation mechanism that can submit operable equipment and its operation guide when an abnormality occurs in plant equipment by inputting a procedure.