Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.