A Method of Fault Classification and Diagnosis Based on Dissimilarity Index

Abstract

The invention discloses a fault classification and diagnosis method based on dissimilarity index, which aims to solve two key problems faced when implementing the fault classification and diagnosis method in the actual industrial process: the number of available training samples for reference fault types is limited and different The sampling data of faults will overlap in the spatial distribution. The inventive method firstly selects the characteristic variable of each reference fault type, and selects the characteristic variable that can best distinguish the fault from normal data. Then, by comparing the distribution dissimilarity between the online fault data window and each reference fault data window using the characteristic variables, the fault type detected online corresponds to the reference fault type with the minimum dissimilarity index. Compared with the traditional classification and diagnosis method, the method of the present invention reduces the variable dimension through variable selection, which not only greatly reduces the constraint of insufficient training data, but also eliminates the "interference" effect of non-characteristic variables. In addition, the method implements fault diagnosis through similar matching of window data in spatial distribution, which can avoid misclassification of overlapping data to the greatest extent.

Description

technical field [0001] The invention relates to an industrial process fault diagnosis method, in particular to a fault classification diagnosis method based on a non-similarity index. Background technique [0002] With the complexity and large-scale trend of the modern industrial process, the requirement for continuous normal operation of the production process is increasing, and more and more attention is paid to timely and accurately diagnosing the faults in the production process. In modern industrial processes, due to the widespread adoption of DCS control systems and advanced measuring instruments, a large amount of sampled data can be stored and measured online in real time. These sampling data contain important information such as whether the production process is normal and whether the product quality is qualified, which provides a solid foundation for the data-driven process monitoring method. Generally speaking, process monitoring mainly includes two aspects: faul...

AI Technical Summary

Problems solved by technology

Compared with the hundreds of measurement points in the modern industrial process, it is conceivable that the data samples of a certain fault type that are allowed to be collected in the actual process are difficult to meet the minimum number of samples required for the sufficient condition of the number of samples

At the same time, due to the coupling between the production process and the control system, there is a large degree of correlation between the measured variables, and different faults may cause the same abnormal changes in some variables

This will lead to overlapping in the spatial distribution of sampled data of different fault types, and the classification model method for classification and diagnosis with a single data point will cause a large number of misclassification and diagnosis phenomena

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