Automated Variational Nonlinear Chirp Mode Decomposition for Bearing Fault Diagnosis

The variational nonlinear chirp mode decomposition (VNCMD) requires initialization of number of modes (NMs) and instantaneous frequency (IF). This article proposes an automated method for NM selection and IF initialization, which works on the scale-space representation-based automated boundary detection in a magnitude spectrum. The proposed automated VNCMD (AVNCMD) method is applied for bearing fault detection in which the kurtosis-based dominant mode selection method is recommended. The instantaneous amplitude and IF with spectral entropy are computed from the dominant mode. Features are given to a feed-forward neural network classifier. The methodology is investigated on two datasets for inner-race, outer-race, and ball-race faults detection. The proposed method classifies inner-race, outer-race, and ball-race bearing faults with 97.52% accuracy and classifies inner-race and outer-race bearing faults with 100% accuracy. The efficacy of the proposed method is compared with the existing methods to justify the superiority.