A self-adaptive multiple-fault diagnosis system for rolling element bearings

The inevitable simultaneous formation of multiple-faults in bearings generates severe vibrations, causing premature component failure and unnecessary downtime. For accurate diagnosis of multiple-faults, machine learning (ML) models need to be trained with the signature of different multiple-faults, which increases the data acquisition time and expense. This paper proposes a self-adaptive vibration signature-based fault diagnostic method for detecting multiple bearing faults using various single-fault vibration signatures. A time-frequency-based hybrid signal processing technique, which involves discrete wavelet transform and Hilbert transform, was adopted for signal decomposition, followed by the implementation of a sliding window-based feature extraction process. Seven optimized metaheuristic algorithms were used to find the best feature sets, which were further used for the training of three ML models. The results show that the proposed methodology has tremendous potential to detect multiple bearing fault conditions in any possible combination using single-fault data. This will be helpful where accessibility to large amounts of data is limited for multiple-fault diagnosis.