Multiple Enhanced Sparse Decomposition for Gearbox Compound Fault Diagnosis

The vibration monitoring of gearboxes is an effective means of ensuring the long-term safe operation of rotating machinery. A gearbox may have more than one fault in actual applications. Therefore, gearbox compound fault diagnosis should be investigated. In this paper, a novel multiple enhanced sparse decomposition (MESD) method is proposed to address multiple feature extraction for gearbox compound fault vibration signals. Through this method, a novel MESD algorithm is utilized to simultaneously separate and extract the harmonic components and transient features of the gear and bearing from the compound fault signal. Three subdictionaries are specially constructed according to the gearbox failure mechanism to accurately extract each feature component. Meanwhile, the generalized minimax concave (GMC) penalty is used as sparse regularization to further ensure the accuracy of sparse decomposition. The simulation and engineering signals of the gearbox validate the performance of the proposed MESD method.