A supervised sparsity-based wavelet feature for bearing fault diagnosis

• Published in: Journal of intelligent manufacturing Vol. 30; no. 1; pp. 229 - 239
• Main Authors: Wang, Cong, Gan, Meng, Zhu, Chang’an
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2019; Springer Nature B.V
• Subjects: Advanced manufacturing technologies; Bearing; Business and Management; Coding; Control; Dictionaries; Fault detection; Fault diagnosis; Machines; Manufacturing; Mechatronics; Processes; Production; Robotics; Signal processing; Sparsity; Wavelet transforms; Supervised sparsity-based wavelet feature (SSWF); Structured dictionary; Sparse coding; Machinery fault diagnosis; Wavelet packet transform (WPT)

This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients...