Feature Enhancement Based on Regular Sparse Model for Planetary Gearbox Fault Diagnosis

Abstract

The planetary gearbox, widely used in many machinery fields, suffers from harmful vibration excited by bearings fault, which always causes machine breakdowns. Thus, fault diagnosis is the necessary approach to keeping machines safe, which often takes fault features, which are extracted from signals, as critical information. However, limited to various interferences caused by gear meshing and background noise, fault characteristic information is always weak and difficult to identify, bringing an increasing emphasis on feature enhancement. In this paper, based on the feature enhancement problem under strong interferences, a sparse regular diagnosis algorithm is studied. Firstly, a new fault sensitivity indicator is constructed to distinguish different periodic impulse signals and enhance the energy of the fault impulse. Combined with tunable Q-factor wavelet transform multi-scale representation, the guided enhanced vector is introduced to guide the directional gathering of the fault frequency band. Then a weighted regular term is constructed to establish the Guided Enhanced Regular Sparse (GERS) model. Iterative soft threshold algorithm is employed to solve the model. Compared with state-of-the-art methods, through numerical simulation and fault experiment, the effectiveness and superiority of the proposed algorithm is verified successfully.