Application of Wavelet Threshold Denoising on Bearing Fault Diagnosis

• Published in: 2019 Chinese Control And Decision Conference (CCDC) pp. 1980 - 1985
• Main Authors: Kai, Wang, Shaobo, Dong, Zilin, Yu, Shijie, Shan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2019
• Subjects: Fault diagnosis; Mathematical model; Mean square error methods; Noise reduction; Rolling Bearing; Rolling bearings; Signal to noise ratio; Threshold Function; Wavelet coefficients; Wavelet Transform
• ISSN: 1948-9447
• DOI: 10.1109/CCDC.2019.8832990

Bearing fault signal is complex and non-stationary, this makes fault feature extraction very difficult. Through investigation of soft threshold function and hard threshold function, a wavelet denoising method based on improved threshold function has been proposed. By this method the feature of fault signal is highlighted and the fault diagnosis effect was improved. This paper takes the measured bearing fault signal as the research object, the fault signal feature frequency component has been found by this method. This paper provides a new idea for fault feature extraction and bearing fault diagnosis.