A Fault Diagnosis Method for Rolling Bearings Based on Multidimensional Vibration Feature Fusion in Time-Frequency Domain

Abstract

The present invention proposes a rolling bearing fault diagnosis algorithm based on multi-dimensional fault feature fusion in the time-frequency domain, aiming at the vibration signals in the four states of normal state, roller fault, inner ring fault and outer ring fault of the rolling bearing in the time-frequency domain respectively The characteristics of faults are extracted by extracting time-domain and frequency-domain features, removing redundancy, and re-integrating ideas to optimize the description of fault characteristics and obtain intelligent discrimination results. First, perform wavelet denoising on the extracted original rolling bearing vibration data, then extract time-domain feature vectors to form a time-domain feature matrix, and extract wavelet packet decomposition and reconstructed coefficient energy moments to form a frequency-domain feature matrix, and further fuse the time-frequency domain matrix , to obtain the multi-dimensional fault feature matrix in the time-frequency domain. Perform de-redundancy processing on the multi-dimensional feature matrix to obtain a new multi-dimensional feature matrix. Then, the multi-dimensional features are fused with the weighted feature index distance, and the state discrimination result of the rolling bearing is obtained through the fused feature index distance.

Description

A Fault Diagnosis Method for Rolling Bearings Based on Multidimensional Vibration Feature Fusion in Time-Frequency Domain technical field 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 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. 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 fai...

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Problems solved by technology

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.

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