JPS61175709A - Estimating system for trouble affecting range of plant - Google Patents

Abstract

PURPOSE:To estimate a trouble affecting range of a plant with higher accuracy by knowing a range where the trouble effect reaches assuredly and a maximum trouble affecting range from the trouble affecting relation among devices, the information on the trouble detection of a sensor and the trouble detecting time point and displaying both ranges. CONSTITUTION:A plant 101 includes its component devices 102 and sensors 103, and the detection signals 105 of sensors 103 are supplied to the corresponding trouble detectors 106 of a trouble detecting equipment 107. Then the signals 108 showing the normal or abnormal states are supplied to a trouble affecting range estimating device 109. The device 109 displays a range where the trouble effect reaches assuredly and the maximum trouble affecting range at the estimated time points supplied from an operator console 110 on a display device 115 based on the type of the trouble, the trouble generated time point as well as the trouble affecting direction and the maximum and minimum trouble affecting times given from an initial data input device 112.

Description

[Detailed Description of the Invention] [Field of Application of the Invention] The present invention provides a system for detecting a problem in which, when a failure occurs in some of the component equipment in a plant, the influence of the failure spreads to any range of equipment in the plant after a certain period of time. This invention relates to a system for predicting the extent of failure of a plant and displaying the state of the predicted influence.

[Background of the invention]

The present inventor has already proposed JP-A-59-9710 as a method for predicting the range of failures in plants. In a system consisting of multiple devices, this method is based on the direct fault propagation relationship (spread direction and propagation time) between two devices, abnormality information obtained from sensors, and abnormality after a certain period of time from the time of detection. The unique feature is that it predicts the range of equipment that will be affected by the failure from the detection location. However, this method does not take into account the possibility that the failure propagation time changes depending on the severity of the failure. Therefore, there was a possibility that the predicted range of failure spread could be incorrect.

[Purpose of the invention]

In order to solve the above-mentioned problems of the prior art, an object of the present invention is to develop a plant capable of displaying an accurate failure spread prediction range even when the failure spread time between devices constituting the plant changes depending on the degree of failure. The object of the present invention is to provide a failure influence range prediction method.

[Summary of the invention]

In order to achieve the above object, the present invention provides a direct fault propagation relationship (propagation direction, maximum propagation time and minimum propagation time) between two devices in a system consisting of a plurality of devices. Based on the abnormality information obtained from the abnormality information and the detection time of the abnormality, it is possible to predict which equipment will be within the range where the influence of the failure will spread from the abnormality detection point within a certain period of time. At this time, the prediction result using the maximum fault propagation time is set as the range that will definitely spread, and the prediction result using the minimum fault propagation time is set as the range that may potentially spread, thereby responding to changes in the fault propagation time. There is.

[Embodiments of the invention]

Hereinafter, one embodiment of the present invention will be explained in detail with reference to FIGS. 1 to 3.

FIG. 1 shows the configuration of an embodiment of a plant system that implements the plant failure influence range prediction method according to the present invention.

In FIG. 1, a plant 101 includes a plurality of component devices 102 and sensors 103 for detecting the status of some of the components. These sensors 103 monitor the operating status of each component 102, such as flow rate, temperature 9
A signal 104 such as a frequency is detected, and a detection signal 105 is output to an abnormality detection device f107 comprising an abnormality detector 106 corresponding to each sensor 103. In the abnormality detection device 107.

A reference signal for determining whether each signal is normal or abnormal is stored in advance, and the reference signal and the detection signal 105 are compared, and a signal 108 indicating whether each detection signal 105 is normal or abnormal is sent to the failure influence range prediction device 109. Output. The failure spread prediction/prediction device [1109] checks the signal 108 at regular intervals, and if it changes from a normal signal to an abnormal signal, stores the time. When the predicted time designation signal 111 is output from the operator console 110, the failure influence range prediction device 109 inputs the signal 108, the time at which the normal signal changes to the abnormal signal, and the initial data input device 112 in accordance with the predicted time designation signal 111. Based on the signal 113 corresponding to the failure propagation direction, maximum propagation time, and minimum propagation time input from The devices within the range where there is a possibility of influence are calculated, and the device number signal 114 is output to the display device 115.

Note that once the initial data is entered, there is no need to re-enter it unless the data is changed.

FIG. 2 is a flowchart showing an example of a processing procedure in the failure influence range prediction device 109 shown in FIG.

Hereinafter, the embodiments of the present invention will be described with reference to the 31st example of the failure propagation relationship of plant component equipment. This will be explained using an example of application to a plant represented by a network of I.

In the network shown in Figure 3, the nodes represent plant components, and the direction of the arrow represents the direction in which the influence of a failure will directly spread.The numbers attached to the branches are such that the left side is the minimum value of the direct failure propagation time, and the right side is the maximum value. represents a value.

Assume now that abnormal signals are being output from devices 1 and 4 in FIG. The elapsed time from a certain reference time to the abnormality detection time is t1=Q seconds, respectively.

Let t, = 100 seconds, and the elapsed time from the reference time to the predicted time specified by the operator console 110, t, = 2.
00 seconds.

Hereinafter, step 116 will be described in which the failure influence prediction range is calculated using the maximum influence time as the direct failure influence time shown in FIG. Using the maximum value of the direct failure propagation time shown in Figure 3, we calculate the devices within the predicted failure propagation range: 1 device 1, 2... , 3,4°10.11 is obtained. This indicates a device for which the influence of a failure is predicted to spread even when the direct failure influence time is the longest, that is, a device for which the influence of a failure is definitely predicted to have a ripple effect at the specified predicted time. The method for calculating the fault spread range when the fault spread time is given is described in detail in Japanese Patent Application Laid-Open No. 59-9710, and will therefore be omitted here.

Next, using the minimum propagation time as the direct failure propagation time in FIG. 2, step 117 calculates the failure propagation prediction 1i[
Let's talk about. Using the minimum value of the direct failure propagation time shown in FIG. 3 to calculate the devices within the predicted failure propagation range, we obtain devices 1, 2, 3, 4, 5°8.9, 10, and 11. Among these, devices 5, 8, and 9, which were not among the devices found in step 116, are devices for which the influence of failure is predicted to occur when the direct failure influence time is the shortest, that is, at the specified predicted time, although it is not certain. Represents equipment that is predicted to have a ripple effect due to failure.

According to the above-mentioned embodiment, (1) it is possible to predict the failure influence range from the abnormality detection point after a specified time from a limited number of sensor information, and (2) it is possible to predict the failure influence range by the specified time. This has the effect of making it possible to distinguish between equipment that is located within a range and equipment that is within a range where a failure may spread.

〔Effect of the invention〕

As explained above, according to the present invention, (1) it is possible to predict the failure influence range from the abnormality detection point after a specified time from a limited number of sensor information, and (2) it is possible to ensure that failure influence occurs by the specified time. It is now possible to distinguish between devices that are in the range where the failure will occur and devices that are in the range where the failure may spread.As a result, it is possible to predict the range of the failure that will be affected, which was not possible with conventional methods, and it is possible to easily take countermeasures when a failure occurs. effective.

[Brief explanation of the drawing]

Fig. 1 is a block diagram of an embodiment of a plant system to which the present invention is applied, Fig. 2 is a flowchart showing the processing procedure in the failure influence range prediction device of Fig. 1, and Fig. 3 is a broom 1 of 1!2 mouth

Claims (1)

[Claims] A sensor that detects the fault spread relationship and anomaly between two devices in a plant failure spread prediction method that predicts the range where the effects of a failure will spread based on sensor information corresponding to a specific device among multiple devices. The method is characterized by predicting which devices are within a range where the effects of a failure will definitely spread from the device in which the abnormality has been detected, and which devices are within a range where the effects of the failure are likely to spread, based on the detection information and detection time of the device. Plant failure spread prediction method.