Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit

• Published in: Journal of intelligent manufacturing Vol. 28; no. 6; pp. 1377 - 1391
• Main Authors: Wang, Cong, Gan, Meng, Zhu, Chang’an
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2017; Springer Nature B.V
• Subjects: Advanced manufacturing technologies; Bearings; Business and Management; Classifiers; Control; Dictionaries; Discrete Wavelet Transform; Energy consumption; Fault diagnosis; Feature extraction; Feature recognition; Frequency distribution; Intelligent systems; Machines; Manufacturing; Mechatronics; Pattern recognition; Processes; Production; Redundancy; Robotics; Roller bearings; Studies; Wavelet transforms; Basis pursuit; Intelligent fault diagnosis; Sparse wavelet energy (SWE); Overcomplete discrete wavelet transform (DWT)

This paper explores sparse time-frequency distribution (TFD) using overcomplete discrete wavelet transform (DWT) and sparse representation techniques. This distribution is discovered for characterizing the periodic transient information embedded in rolling element bearings and extracting effective f...