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Harmonics-to-noise ratio guided deconvolution and its application for bearing fault detection

In this paper, a precise and intelligent deconvolution process is developed in order to further extend its applications in discovering bearing faults and characteristics. A new index, harmonics-to-noise ratio (HNR), is introduced to extract the period. At the same time by combining HNR with the characteristic of kurtosis which is sensitive to the impulsivity of the fault signal, a novel deconvolution norm named HNR-guided deconvolution (HNRGD) is established to improve the performances of detecting the periodic impulses without requiring any prior knowledge. The effectiveness of the proposed method is validated by both simulation and experiment. The results demonstrate the superiority of HNRGD in the extraction and diagnosis of REBs faults compared with the original MED and MCKD.