Rotating machinery fault diagnosis method based on independent component analysis and correlation criteria

Abstract

The invention relates to signal processing and artificial intelligence, and aims to provide a rotating machinery fault diagnosis method based on independent component analysis and correlation criteria. The method comprises the steps of: measuring multi-channel vibration signals of the rotating machine, and performing transformation through an independent component analysis algorithm; selecting a separation signal based on a correlation criterion and a Bayesian information criterion, and extracting a fault feature vector; and training and diagnosing the diagnosis model of a rotary machine support vector machine, inputting the feature vector R into the model to perform fault diagnosis on the rotary machine, classifying diagnosis results according to a preset judgment condition, and giving analarm. According to the method, the problem that previous independent component analysis is only applied to artificial diagnosis and is rarely applied to artificial intelligence diagnosis is solved,and the accuracy of fault diagnosis of the rotary machine is improved to a greater extent, so that the method has a very good use value.

Description

technical field [0001] The invention relates to the application of signal processing and artificial intelligence in mechanical fault diagnosis, in particular to an artificial intelligence fault diagnosis method based on independent component analysis and correlation maximization criterion for rotating machinery. Background technique [0002] Rotating machinery is a very important and widely used type of mechanical equipment in the industrial field, such as fans, water pumps, compressors, etc., and plays an important role in various industrial fields. During the working process of rotating machinery, it is inevitable that large or small faults will occur. These faults may cause economic losses in the slightest, and cause casualties in severe cases. Therefore, the fault diagnosis of rotating machinery plays a very important role in the industrial field. The research on fault diagnosis methods has never stopped. [0003] At present, most of the fault diagnosis of rotating mach...

AI Technical Summary

Problems solved by technology

[0004] Due to the amplitude and order uncertainty of independent component analysis, it is difficult to be used as the input vector of artificial intelligence diagnosis algorithm, so there is currently no artificial intelligence diagnosis algorithm based on independent component analysis for rotating machinery

Images