Sparsity signal decomposition via GMC penalty for aero-engine accessory gearbox fault diagnosis

Vibration signals of faulty gearboxes usually consist of the meshing components and the periodic transient components, contaminated by noise. Sparsity assisted signal decomposition is an effective technique to decompose a signal into morphologically distinct components based on sparse representation and optimization. This proposes a sparsity-enhanced signal decomposition method utilizing the generalized minimax-concave (GMC) penalty as a nonconvex regularizer to enhance signal sparsity, different from the classical sparsity-assisted signal decomposition methods, aiming at improving the decomposition accuracy for gearbox fault diagnosis. Simulation studies demonstrate that the proposed sparsity-enhanced signal decomposition method effectively decomposes the faulty gearbox signal into meshing components and periodic transient components. Validations using engine data verify that the proposed method can accurately estimate the fault-relevant periodic transient components, demonstrating that the proposed method is a promising tool for aero-engine accessory gearbox fault diagnosis.