JPS58115599A - Plant diagnosis equipment - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a plant diagnosis device that identifies abnormal events in a plant online in real time in order to contribute to improving the operational reliability and availability of large-scale plants.

Conventionally, there was a device for this ridge as shown in Figure 1 of the cocoon. In the figure, (l) is a data acquisition device 1 (for example, an analog-to-digital converter) for reading process data, and (2) is a data acquisition device 1 (for example, an analog-to-digital converter) for reading process data, and (2) compares the process data with a reference value, and if it is within the allowable range, IO'', the range an arithmetic processing unit for converting to 1'' if it is outside 1. (3) is the arithmetic processing unit 1 (
2) is a memory value 9M1 that stores the result of the arithmetic processing.
(4) is the storage device I! that stores the cause and effect tree (hereinafter referred to as CCT). 2, (6) is a storage device 1lit 1
(3) and g (4) and the process information stored in C
The diagnostic processing device (6) that uses CT to identify the first cause is a cathode ray tube display device for displaying the diagnostic results.

Process data 3El (1■182...,N)
shall be.

xl is quantized by the data acquisition device (1). Using the quantized data X1 as input, the arithmetic processing unit (2) performs the processing shown in equation 0, and the result is H(God IJ, . . .
.

M) and the event occurrence time are stored in the storage device (3).

Here, 81 is called the xI status, and the time when the status becomes 1 is called the event occurrence time.

An example of a portion of the OCT stored in the storage device (4) is shown in Figure G2. Among the 8th causes, 菖60M is a diagnostic message, V is a time delay I, and G11-G is an AND gate.

Gtl-egg indicates an OR gate. In CCT, the position where G1 is defined is called a node.

81. in the ms diagram. ...Jf17 is stored in the storage device U). CCT describes an abnormality propagation sequence as a logical relationship between statuses, but if the event propagation time between statuses is important, a time delay (f in FIG. 2) is used. CCT execution processing is performed using memory fi & (8)
Based on the information from and (4), the diagnostic processing device φ) performs n
Ru. Disturbance I! l The following processing is performed to search for the cause.

First, sub-CCT to be searched from among many CCT i¥
Mnemonic to extract parts! (3) Among the n entry nodes (specified in advance as nodes to start analysis), one whose status has changed is detected, and the cross tree to the lower node side starts from that node. Then check whether it is the first gate, an AND gate, or an on gate. Next, the relationship between the state of each input of the gate and the event occurrence time, which is the plant observation data written in the storage device (3), is stored in the CCC written in the storage device & (43).
Check if the models match. If the logic is established, the storage device (
4] The next note to be searched is determined according to the CCT model stored in [4]. The order of search nodes is to start with the entry node, search for all nodes that are children of the entry node, and then search for all nodes that are grandchildren of the entry node.

In this way, in order, for each gate connected below the entry node, determine whether the node on the input side of each gate is false (unmatched) or true (matched), and connect the true branches. When the expansion reaches the primary node, the logic of this entry node is established and it is determined that the cause identification was successful. A time delay between events is set in each part of the CCT, but if an event actually progresses faster than this, the nodes below that are not expanded.

That is, when it is found that there is no cut set for the entry node during expansion, the diagnosis is stopped at that point. For example, if one of the AND logic is "false", the following expansion will not be performed. Therefore, one diagnosis was discontinued.
If the process is completed or completed, the analysis from the primary entry node is executed taking priority into consideration. The above process is performed in the 8th step.
The CCT shown in the figure will be explained. In Figure 8 * Ml #
M4 * M1γ1M1・ is the diagnostic message a ag
e GM# G11t Gts is an AND gate.

Gxa#'! 4 e Gjl a B, e c,
, t'#e'11 is a logical OR gate, O is an observed node, and O is an unobserved node.

An example of the status of observable nodes corresponding to the CCT of FIG. 8 is shown in diagram liA. In the llN4 diagram! The variable is within the alarm level. H indicates that the variable exceeds the high alarm level. In T, the observation node matches the requirement condition. F indicates that the observed node does not match the required conditions.

- When v6 exceeds the alarm level, start searching to find out which unpleasant cause is causing the C failure. ■＠；Entry・
Node ■ The node status in FIG. 4 indicates whether the conditions of the node have been added (Ti is true) or not (Fi is true).

If true, this indicates that the process has exceeded the alarm level.

■ Starting from v6, expand the CCT downward.

VaJjl True Dyeing Table (T Nimam Cut Set)
Search for. Expressed as a character expression, it can be written as follows.

For an entry node whose cause has been successfully identified, the logic is followed upwards from that node to analyze the progress state of the set disturbance. That is, it is checked whether the disturbance has spread by n in the upward direction, and assuming that the disturbance has spread further, a check is made to determine whether the nodes above the topmost active node will change their state after some time. Therefore, first check the type of gate immediately above the entry node. Next, the input status of that gate is checked, and a search is made to see if the output status, which is currently Fal..., becomes Trm. as time passes.

(If there is no gate, the status of the observable node above the entry node) When the above prediction process is applied to the CCT of the i48 diagram, the result is as follows.

■Starting from v6, search CCT upwards. 1 that the gate immediately above v6 is an AND gate
iiiB. The output node υ4 becomes true because Vma is true due to vl8 and v6 is true. Going further up, since it is an OR gate, theoretically vl is true, but from Table 1, the observed value of Vl is not true because it is a time window. In principle, prediction is performed upwards, but for AND gates, processing is performed downwards. (For example, at the first moment of determining the status of υ4, V7.
when checking the Vlg status).

The prediction process in the CCT shown in FIG. 8 can be described using a literal expression as follows.

As described above, through the cause identification and prediction processing, it is possible to determine a tree that sequentially represents the disturbance from its occurrence to its future propagation.

Restore the message contents and variables connected to this tree. Then, the following information is leased to the cathode ray tube display device (6).

(1) Messages specified as part of the tree that has progressed so far (@x s Ml7. M4 message)
(2) Messages that will become active in the near future (
(Example: Ml) (3) List of the number of flights related to a part of the tree determined in the previous procedure The number of CCTs used for plant diagnosis is shown in Figure 8 for a large-scale system such as a nuclear power plant. CC shown
It is several thousand times that of T. As described above, the CCT execution processing method adopted in conventional plant diagnostic equipment does not create object modules directly, but executes them in an interleaved manner, so there are many unnecessary judgment statements. However, the efficiency was poor. Furthermore, diagnostic processing was not divided by function.

Therefore, in order to perform diagnosis of a large-scale plant in real time, it is necessary to use an expensive large-scale computer as the diagnosis processing device (5) in Figure 1, and therefore it is necessary to improve the reliability of the diagnosis system. However, there are disadvantages in that it is difficult to construct a low-cost and highly reliable diagnostic system, such as the high cost of multiplexing diagnostic processing devices.

This invention was made in order to eliminate the drawbacks of the conventional ones as described above, and by distributing the large-〇CCT to a plurality of inexpensive arithmetic processing units according to processing functions,
The purpose of this project is to provide a plant diagnostic device that is inexpensive, has high diagnostic efficiency, and is highly reliable as a total system.

An embodiment of the present invention will be described below with reference to the drawings. In Figure 1, (1), (:2), (3),
(4) and (6) are of the same type as the conventional one, and (7) uses the process information and CCT stored in memory classes fll (3) and 2 (4) to (8) is the arithmetic processing unit for the logical operation unit that executes the calculation of the logical operation value of the operation unit m unit (each calculated by n) - the logical operation value (No Y status and estimated event occurrence time) ), and (9) compares the observation f-1 from the storage device (3) and the -logical operation value from the storage device (1) with respect to the CCT logic obtained from the arithmetic processing device (7). This is an arithmetic processing unit for the diagnostic analysis unit that detects changes in entry nodes based on coincidences, mismatches, etc., identifies causes, and makes predictions.

The CCT processing method of the present invention will be explained. The CCT execution processing data flow is shown in FIG. 4, and the present invention is particularly characterized in that the diagnostic processing is divided into a logic operation section and a diagnostic analysis section. We adopted a method in which the logical operation section is separated and all logical operations are executed every sampling period, which requires less computer processing time than the conventional event processing method that executes logical operations in response to changes in entry nodes. This is because the diagnostic analysis section is simplified and more advanced diagnosis can be incorporated by making it shorter and by removing the logic/arithmetic section.

In the logic operation section arithmetic processing unit (7), the view of the primary node in the storage device (3) is 1llIO! With i as input, CCT gate logic of storage device ff (4) -ζ
Therefore, all other logical operations (calculation of the status of the flea node and the estimated event occurrence time) are executed cyclically each time and stored in the storage device (8). This calculation is.

The calculation is performed upward every sampling period regardless of the status value or change of the observed value of the primary node or node. The program processed here is the target cc'
Once r is determined, it is uniquely determined, so CC
A method is adopted in which an object module is created for each T module.

In the diagnostic analysis unit arithmetic processing unit (9), the storage device f (8)
Diagnosis processing of CζCCT is performed based on the analysis results of the logic operation unit stored in the memory, and the analysis contents are divided into entry node change detection, cause identification, and prediction.

Detection of changes in entry nodes is carried out by checking the status of entry nodes at every sampling period to determine whether the plant is normal, an abnormality has occurred, an abnormal event continues, or an abnormality has been recovered.1 To determine the necessity of diagnostic processing. Ru. That is, the observation results of the current sampling and the previous observation results are checked from the storage device (<8), a status indicator is assigned based on the contents of the check, and diagnosis such as cause identification, prediction, etc. or the next entry is performed according to the indicator.・Search for nodes.

Green cause identification uses a storage device (a) that stores observation data.
), the CCT model (4), and information from the storage device (8) that stores the calculation results of the logical operation unit.

Each node Y calculated from the observed values of the fry-marry node
Check the correctness of the status by checking the observed value of the node above it, and for the puff from the confirmed cause event to the entry node, the message assigned to that path is displayed on the CRT display (6 ).

In prediction, when the operation result of entry no. 1z is [j, the effect it has on the upper node is -
Bell. Here too, the correspondence between the physical calculation results and the observation results is checked, and the message assigned to the upper node is displayed on the cathode ray tube display (6) as a mouth-shaped b message,
If there is a mismatch, it is displayed as a predicted message.

For information essential to the diagnostic analysis section, a table (database) is created for each CCT module, and the CCT program interprets and analyzes it.

The advantages of the CCT execution processing method adopted this time are described below.

■Efficiency in diagnosis is improved by dividing the system into a logical operation section and a diagnostic analysis section, and employing a method of directly converting it into an object module and a method of interpreting it interpretively depending on the processing content.

In other words, if all iζ are processed in an interleaved manner as in the past, a large number of unnecessary judgment statements will be included in the logical operation, resulting in poor efficiency.

In the 8611 calculation unit, the event occurrence time of the father node is calculated uniformly in advance based on the event occurrence time and time delay of the sun node, so there is no need to consider the time delay in #danshuro. It's over.

In the above embodiment, the arithmetic processing unit (7) that performs logical operations and the arithmetic processing unit (9) that performs diagnostic processing are expressed as separate units. 1 as hardware if it has the function
Even as a single device, the same effects as those of the above embodiment can be achieved.

In view of the above, according to the invention of C, since an execution processing method that allows calculations to be performed for each processing function is adopted, it is possible to configure the plant diagnostic device to share the load among a plurality of processing devices. Therefore, by combining a plurality of inexpensive arithmetic processing devices, it is possible to achieve effects such as achieving higher performance than expensive arithmetic processing devices and making it possible to easily and economically improve the reliability of the diagnostic system.

[Brief explanation of the drawing]

Fig. 1 is a block diagram showing a conventional plant diagnosis device, Fig. 2 is an explanatory diagram of CCT showing an example of plant diagnosis logic, Fig. S is a CCT diagram for explaining an example of CCT execution processing,
FIG. 4 is a diagram showing an example of the status of observable nodes corresponding to the CCT of Nishiki 8, and FIG. 5 is a block diagram of a plant ridge cutting device showing an embodiment according to the present invention. In the figure, (1) is a data collection device, (2) is a first arithmetic processing unit, (3) is a first storage device, (4) is a second
storage device, (5) is a diagnostic processing unit, (June is a cathode ray tube display device, (7) is an arithmetic processing unit that performs logical operations on nodes, and (8) stores the logical operation values obtained in (7). The eighth storage device, (9) is the storage device (3), (4)
, (8) is a diagnostic processing device that performs calculations based on information from (8). In the drawings, the same reference numerals indicate the same or equivalent parts.

Claims (1)

[Claims] By processing a cause-and-effect tree that distributes the propagation sequence of various abnormal events that occur in a plant using logical formulas, we can identify the cause of an abnormality online in real time, and support the comprehensive judgment of plant operators. In a plant diagnosis device that enables highly reliable operation, the execution process of the above cause-effect tree is performed by a logic operation section that calculates the logic operation value of each node, and causes identification and prediction based on the results. The processing of the cause-and-effect tree is distributed and executed in separate arithmetic processing units, and the processing time of the computer is shortened. Plant diagnosis i1゜ characterized by its reliability.