Abstract
Rolling element bearings are critical components of rotating machines since the failure of rolling element bearings may cease the functioning of the entire equipment. The damages observed due to bearing failures are expeditious in nature and hence the need to develop an effective prognostic methodology becomes a requisite to prevent the sudden machinery breakdown. The performance degradation assessment and accurate determination of remaining useful life are the two key issues in prognostics of rolling element bearings. This paper proposes a degradation indicator based on self-organising map for the performance degradation assessment of bearings and later support vector regression is utilised to estimate the remaining useful life of bearings. The time-domain and frequency domain features extracted from the raw bearing vibration signals are supplied to the self-organising map classifier to achieve the degradation index termed as self-organising map-minimum quantisation error evolution in the paper. For estimating the remaining useful life of bearings, first the central trend of minimum quantisation error is extracted to achieve the feature vector defined as bearing health index in this work. The bearing health index is then used as input and the life percentage of the bearing is set to output in order to build the support vector regression prediction model for remaining useful life estimation of bearings. The proposed method is validated on the vibration signatures collected in a bearing test rig. The results show that the advocated method can efficiently track the evolution of deterioration and predict the remaining useful life of bearings.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
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.