Abnormal symptom-triggered remaining useful life prediction for rolling element bearings

Remaining useful life prediction of rolling element bearings is significant to improve the safety and reliability of engineering systems. It is a vital issue to perceive abnormal symptoms for remaining useful life prediction so as to set the occurrence time as the first prediction time. Traditional methods need to define indicators manually to determine the abnormal symptoms, which rely on a lot of domain knowledge and expert experience. This paper proposes a novel abnormal symptoms-triggered remaining useful life prediction approach for rolling element bearings. An adaptive kernel spectral clustering model is constructed in the abnormal symptoms-triggered remaining useful life prediction approach to adaptively detect the abnormal symptoms in real time from multi-dimensional degradation features extracted by integrating time-domain, frequency-domain, time-frequency, and time-series analysis. The occurrence time of abnormal symptoms is set as the first prediction time of the remaining useful life predictor, which is a new type of particle swarm optimization-quantile regression neural network. A benchmarking dataset of rolling element bearings is adopted to evaluate the proposed abnormal symptoms-triggered remaining useful life prediction approach. Experimental results show that this abnormal symptoms-triggered remaining useful life prediction approach is superior to many other prognostic approaches.