Gear Fault Diagnosis Based on Dual Parameter Optimized Resonance-Based Sparse Signal Decomposition of Motor Current

• Published in: IEEE transactions on industry applications Vol. 54; no. 4; pp. 3782 - 3792
• Main Authors: Chai, Na, Yang, Ming, Ni, Qinan, Xu, Dianguo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Decomposition; Decomposition level; Fault diagnosis; Feature extraction; gear fault detection; Gears; Kurtosis; Meshing; motor current signature analysis (MCSA); Nondestructive testing; parameter optimization; Parameters; Permanent magnet motors; Permanent magnets; Q-factor; resonance-based sparse signal decomposition (RSSD); Resonant frequency; Search algorithms; Signature analysis; Swarm intelligence; Synchronous motors; Torque; Vibrations; Wavelet transforms
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2018.2821099

Motor current signature analysis (MCSA) provides a nondestructive and remote approach for a gear fault diagnosis. However, in addition to the fault-related components, motor current in the faulty gear system also contains the eccentricity-related components and gear meshing-related components, which contaminate the fault features and increase the difficulty of fault diagnosis. To extract fault features from these interferences, this paper proposes the dual parameters optimized resonance-based sparse signal decomposition (RSSD) method, which can decompose a complex signal into a high- and low-resonance component with two sets of overcomplete wavelet bases. After the decomposition, the fault-related components, which have short duration, will exist in low-resonance component. The novelty is that the wavelet bases related parameters, Q-factors, and decomposition levels are chosen automatically based on artificial bee colony algorithm to obtain the optimal decomposition results instead of chosen subjectively. Kurtosis of the low-resonance component is employed as optimization index. The proposed method is then verified on the gear fault-diagnosis platform, which consists of two permanent magnet synchronous motors and a pair of gears with transmission ratio of 3:2, and its effectiveness over some existing methods under different operating conditions is also validated.