Sparse dictionary design based on edited cepstrum and its application in rolling bearing fault diagnosis

• A novel dictionary design method is proposed for impact feature extraction. • Modal parameters identification errors are corrected by quantitative compensation. • A segmental matching pursuit algorithm with fast calculation speed is applied. • Simulations and experimental tests verify the effectiveness of proposed method. Rolling bearing with a localized defect usually generates periodically impact vibration responses, which carry important information for bearing fault diagnosis. Due to the inevitable noise disturbances, extracting accurate impact features of faulty bearing is still a hard task. In view of the superiority of sparse decomposition on feature extraction, a novel sparse dictionary design method is proposed based on edited cepstrum to improve the precision of feature extraction. The impulse response function is selected as sparse atom, which better reflects the structure and inherent modal characteristics of the faulty bearing. The modal parameters are directly identified from the deconvolved fault signal by edited cepstrum. Identification errors caused by the cepstrum windowing are corrected by quantitative compensation, which further improves the accuracy of dictionary design. A segmental matching pursuit algorithm is applied to speed sparse coefficients solving and fault features reconstruction. A series of simulation analyses verify the proposed method's effectiveness, anti-noise performance and robustness. Experimental tests on pure rolling bearing and gearbox bearing further verify the method's effectiveness under different working conditions. Additionally, comparisons with an improved spectral kurtosis method and an edited cepstrum methodshow the proposed method be more reliable in diagnostic performance.