Fault diagnosis method and system for complementary classification regression tree based on differential evolution

Abstract

The invention relates to a fault diagnosis method and system for a complementary classification regression tree based on differential evolution. The method comprises the steps of obtaining a sample set, wherein the sample set comprises sample signals corresponding to various fault types, and each sample signal is an operation signal of the equipment under the corresponding fault type; analyzing each sample signal in the sample set to obtain a sample feature vector set composed of all sample feature vectors; obtaining a complementary classification regression tree model by taking a genetic algorithm as a differential evolution basis according to the sample feature vector set; wherein the complementary classification regression tree model comprises an original classification regression treeand a complementary classification regression tree; determining an optimal classification regression tree in the complementary classification regression tree model based on the sum of Gini indexes ofall leaf nodes of the classification regression tree and the number of the leaf nodes to obtain a fault diagnosis model of the equipment; and carrying out fault diagnosis on the equipment by adoptinga fault diagnosis model of the equipment based on the operation signal of the equipment. According to the invention, the equipment fault diagnosis performance can be improved.

Description

technical field

[0001] The invention relates to the field of fault diagnosis, in particular to a fault diagnosis method and system based on a complementary classification and regression tree of differential evolution. Background technique

[0002] With the development of machine learning and intelligent optimization technology, the demand for intelligent monitoring services for operating equipment status identification is increasing. The classification and regression tree algorithm has the advantages of simple and easy to implement, strong explanatory, graphical and other advantages, and is often used in monitoring the health of operating equipment. However, due to the influence of data noise and human experience, the features extracted from the monitoring signals of operating equipment are often huge, and invalid and redundant features are prone to affect the diagnostic performance of the model. In order to obtain better diagnostic model performance, intelligent optimizati...

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