Rolling bearing fault diagnosis method based on time-frequency domain multidimensional vibration feature fusion

Abstract

The invention provides a rolling bearing fault diagnosis algorithm based on time-frequency domain multidimensional fault feature fusion. Aiming at the respective features of vibration signals of a rolling bearing in a normal state, a roller fault state, an inner ring fault state and an outer ring fault state in a time-frequency domain, through extraction of time domain and frequency domain features, redundancy removal and re-fusion, fault features are described in an optimal way to obtain an intelligent judgment result. First, wavelet de-noising is performed on extracted original rolling bearing vibration data; then, time domain feature vectors are extracted to form a time domain feature matrix, and coefficient energy moments after wavelet packet decomposition and reconstruction are extracted to form a frequency domain feature matrix; and the time and frequency domain matrixes are further fused to obtain a time-frequency domain multidimensional fault feature matrix. Redundancy of the multidimensional feature matrix is eliminated to obtain a new multidimensional feature matrix. Then, information of multidimensional features is fused with a weighted feature index distance, and a state judgment result of the rolling bearing is obtained through the feature index distance obtained through fusion.

Description

technical field [0001] The invention belongs to the field of automatic detection and pattern recognition, and in particular relates to fault diagnosis and intelligent recognition methods of rotating machinery. Background technique [0002] The fault diagnosis of rolling bearings probably started in the 1960s. After decades of rapid development, it has now become a comprehensive applied discipline that combines the fields of mechanical detection, automatic control and pattern recognition. [0003] As a key component in mechanical equipment, rolling bearings play a vital role in the normal operation of the mechanical system. There are many factors that affect the running state of rolling bearings, such as temperature, mechanical and environmental factors. Some failures are instantaneous, while others are caused by slow and long-term degradation. The resulting rolling bearing failures are diverse, and the severity of the failures is also different. . Rolling bearings are comp...

AI Technical Summary

Problems solved by technology

[0005] The signal characteristics reflected by a single time domain feature and frequency domain feature are not comprehensive, the time domain feature cannot reflect the vibration information in the frequency domain, and the characteristic trend of the time domain cannot be reflected in the frequency domain analysis.

In the past fault diagnosis, the features of a single domain were extracted, or a small number of typical features were extracted for diagnostic analysis, and the diagnostic accuracy was limited. This urgently requires a more comprehensive diagnostic algorithm to achieve a breakthrough in diagnostic accuracy.

Images