A Method of Fault Classification and Diagnosis Based on Weighted Neighbor Decision

Abstract

The invention discloses a fault classification and diagnosis method based on weighted neighbor decision-making, aiming at carrying out variable weighting on various faults by using a neighbor component analysis algorithm, and on this basis, diagnosing the fault type through the weighted neighbor distance. Specifically, the method of the present invention first optimizes corresponding weighted vectors for each reference fault type one by one by using a neighbor component analysis algorithm. Then, the weighted neighbor distance between the weighted samples is calculated, so as to diagnose the fault type to which the online fault data belongs in real time. Compared with the traditional method, the method of the present invention is based on the neighbor relationship whether it is finding the weighted vector of each fault or diagnosing the fault type online, and does not need a relatively sufficient number of available training samples. In addition, the main idea of ​​the method of the present invention is to optimize the weighting coefficients of the variables corresponding to each fault type, so as to highlight the variation of the characteristic variables of each fault. Therefore, the method of the present invention is a more preferred fault classification and diagnosis method.

Description

technical field [0001] The invention relates to a data-driven fault diagnosis method, in particular to a fault classification diagnosis method based on weighted neighbor decision-making. Background technique [0002] In order to ensure safe production and maintain stable product quality, it is necessary to accurately diagnose the faults that occur during the operation of the production object. It can be said that as long as there is production, process monitoring will always be a research topic of widespread concern in the industry and academia. In the existing scientific research literature and patent materials, research on fault detection emerges in an endless stream. In contrast, there are only a handful of research results on fault diagnosis. From the requirements of process monitoring tasks, both fault detection and fault diagnosis are indispensable. Generally speaking, the task of fault detection is to tell us that there are abnormal conditions in the production pro...

AI Technical Summary

Problems solved by technology

This is mainly because these algorithms need sufficient training data to ensure the accuracy of the model when establishing a classification model, and they are usually not suitable for fault classification and diagnosis

Images