An Approach to Recognize Combined Faults of Rolling Bearing by Combing Discrete Wavelet Transform and Generalized S Transform

• Published in: Journal of failure analysis and prevention Vol. 23; no. 1; pp. 258 - 270
• Main Authors: Yu, Mingyue, Yang, Chunxue, Liu, Liqiu, Su, Jingwen
• Format: Journal Article
• Language: English
• Published: Materials Park Springer Nature B.V 01.02.2023
• Subjects: Discrete Wavelet Transform; Fault detection; Feature extraction; Frequencies; Roller bearings; Time-frequency analysis
• ISSN: 1547-7029; 1864-1245
• DOI: 10.1007/s11668-022-01571-x

To properly identify the combined faults of rolling bearings, the paper has proposed a new approach to extract the fault characteristics of rolling bearings based on the combination of discrete wavelet transform (DWT) and generalized S transform (GST). To effectively separate the signals of different frequency bands, original acceleration signal was subjected to wavelet decomposition to obtain approximate and detailed signals. Furthermore, a time–frequency analysis was given to approximate and detailed signals through generalized S transform due to its excellent time–frequency property. At last, according to time–frequency slice diagram of GST, feature frequency of the rolling bearing could be effectively extracted under different combined fault types. The result indicates that the combination of wavelet transform and generalized S transform can identify the combined fault types for bearings more precisely than generalized S transform alone.