The diagnosis of gear and bearing compound faults via adapted dictionary-free orthogonal matching pursuit and spectral negentropy

The diagnosis of gear and bearing compound faults remains a challenge in severe working conditions. Adapted dictionary-free orthogonal matching pursuit (ADOMP) can reconstruct the fault signal more flexibly without predefined dictionaries and maintain the majority of the original information, but it lacks the ability to effectively identify the fault-related atoms. A novel atom selection strategy is proposed based on the spectral negentropy of the squared envelope spectrum (SN-SES), which is sensitive to signal periodicity. The proposed method can isolate atoms according to the characteristics of gear and bearing vibration signals. To better match the fault-related atoms, the minimum entropy deconvolution adjusted (MEDA) method is utilized to preprocess the raw signals, in which the filter length as the key parameter is optimized by SN-SES for impulsiveness enhancement of each single fault. The results of simulation analysis and experimental verification confirm the superiority of the proposed compound-fault diagnosis method.