A Multiperiodicity-Induced Sparse-Fidelity Representation Model for Compound Fault Diagnosis of Wind Turbine Gearbox
Bo Zhang, Wei Teng, Deyi Fu, Ting Xu, Dikang Peng, Lice Gong, Tao Jin
Abstract
Compound faults are common in wind turbine gearbox due to complex structure and harsh operating condition, which are represented by the bearing-gear fault and gear-gear fault <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . It is a challenging task to accurately detect multiple faults from on-site vibration signal potentially contaminated by intensive background noise. To address this issue, a multi-periodicity induced sparse-fidelity representation model is proposed in this paper. The proposed method is based on the periodicity-induced overlapping group shrinkage (POGS) model with the constraint of the sparsity within and across groups (SWAG). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l<sub>p</sub></i> -norm and proposed reweighted minimax-concave penalty function with adjustable order are adopted for SWAG constraints. The hanning sequence is utilized as the periodicity-induced sequence in the POGS model. The closed-form solution of the proposed method is deduced using the majorization minimization algorithm. Eventually, a weighted strategy for the decomposed results is inferred to prevent over-decomposition issue. The proposed method is validated by simulation signals, experimental signal and on-site signal of actual wind turbine, which indicate that the characteristics of compound fault can be extracted plainly and the energy-loss is reduced effectively.