Adapted dictionary-free orthogonal matching pursuit and 0-1 programming to solve the isolation and diagnosis of bearing and gear compound faults

In the fault diagnosis of bearings, the high flexibility of the asymmetric Gaussian chirplet model enables the adapted dictionary-free orthogonal matching pursuit to manifest good performance. Since this method does not rely on predetermined dictionaries, it has potential advantages as well, especially for the impulses caused by compound faults that are multiple type's combination. Benefiting from the sparse representation architecture, the fault isolation can be skillfully converted into a 0-1 programming problem for elements selection in sparse vector, which may become one of the breakthroughs in solving compound faults' isolation. Consequently, this paper attempts to give a solution using signal processing. Specifically, the maximum entropy deconvolution adjusted technique is used for preprocessing, which includes noise reduction and impulsiveness enhancement. Notch filter and spectral subtraction is utilized for impulses screening and extraction, and spectrum is employed for fault diagnosis. Simulation analysis and experimental tests verify the proposed method, whose results illustrated the potentiality to response the compound fault isolation and diagnosis.

Automated summary

This paper presents a signal processing-based method for bearing fault diagnosis, utilizing an asymmetric Gaussian chirplet model and sparse representation architecture to effectively address pulse issues caused by compound faults. The fault isolation is transformed into a 0-1 programming problem, achieving efficient fault diagnosis through maximum entropy deconvolution adjusted technique and spectrum analysis. Simulation analysis and experimental tests confirm the potential of the proposed method for compound fault isolation and diagnosis.