A frequency slice wavelet transform based on wavelet de-noising using neighboring coefficients method and its application in feature extraction of rolling bearing’ early weak fault

The fault feature of the rolling bearing is difficult to extract when weak fault occurs and interference exists. The tunable Q-factor wavelet transform (TQWT) can effectively extract the weak fault characteristic of the rolling bearing, but the manual selection of the Q-factor affects the decomposition result and only using TQWT presents interference. Aiming at the above problems, an adaptive tunable Q-factor wavelet transform (ATQWT) and spectral kurtosis (SK) method is proposed in this paper. Firstly, the method applies particle swarm optimization (PSO) to seek the optimized Q-factor for avoiding manual selection, which uses the kurtosis value of the transient impact component as the particle fitness function. The rolling bearing fault signal is decomposed into continuous oscillation component and transient impact component containing fault feature by the optimized Q-factor. Then, due to the presence of interference in the decomposition result of ATQWT, the SK analysis of the transient impact component is used to determine the frequency band of periodic impact component characterizing fault feature by fast kurtogram. Finally, the band-pass filter established through the above frequency band is employed to filter the interference in the transient impact component. Simulation and experimental results indicate that the ATQWT can highlight the periodic impact component characterizing rolling bearing fault feature, and the SK can filter interference in the transient impact component, which improves feature extraction effect and has great significance to enhance fault diagnosis accuracy of the rolling bearing. Compared with EEMD-TQWT and TQWT-SK, the fault feature extracted by the proposed method is prominent and effective.

Download Full-text

Feature Extraction for Weak Fault of Rolling Bearing Based on Hybrid Signal Processing Technique

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8484102 ◽

2018 ◽

Author(s):

Yang Bao-Ping ◽

Ding Ru-Chun ◽

Zhou Feng-Xing ◽

Xu Bo

Keyword(s):

Signal Processing ◽

Feature Extraction ◽

Rolling Bearing ◽

Processing Technique ◽

Signal Processing Technique ◽

Weak Fault

Download Full-text

Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network

Shock and Vibration ◽

10.1155/2020/6380486 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Junfeng Guo ◽

Xingyu Liu ◽

Shuangxue Li ◽

Zhiming Wang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Real Time ◽

Rolling Bearing ◽

Mechanical Equipment ◽

Time Frequency ◽

The Impact

As one of the important parts of modern mechanical equipment, the accurate real-time diagnosis of rolling bearing is particularly important. Traditional fault diagnosis methods have some disadvantages, such as low diagnostic accuracy and difficult fault feature extraction. In this paper, a method combining Wavelet transform (WT) and Deformable Convolutional Neural Network (D-CNN) is proposed to realize accurate real-time fault diagnosis of end-to-end rolling bearing. The vibration signal of rolling bearing is taken as the monitoring target. Firstly, the Orthogonal Matching Pursuit (OMP) algorithm is used to remove the harmonic signal and retain the impact signal and noise. Secondly, the time-frequency map of the signal is obtained by time-frequency transform using Wavelet analysis. Finally, the D-CNN is used for feature extraction and classification. The experimental results show that the accuracy of the method can reach 99.9% under various fault modes, and it can accurately identify the fault of rolling bearing.

Download Full-text