Assessment of rolling element bearing degradation based on Dynamic Time Warping, kernel ridge regression and support vector regression

Abstract

An accurate and reliable forecast for fault/degradation trend has an enormous significance in predictive maintenance of industrial systems. Prognosis of bearing is essential for efficient implementation of condition based maintenance of rotating machinery to prevent sudden/unexpected breakdown. The Dynamic Time Warping (DTW) algorithm is proposed for the first time for bearing prognosis assessment and health trend prediction of rolling element bearing. The Dynamic Time Warping based value is then applied to obtain a Confidence Value (CV) to standardize the degradation process. The range of the parameter CV is zero to unity. The various degradation stages are recognized by using CV and the fault frequencies are obtained from the envelop spectrum. Further, the Kernel Ridge Regression (KRR) and Support Vector Regression (SVR) methodologies are applied on the DTW feature to forecast the bearing degradation trend. The effectiveness of DTW-KRR and DTW-SVR is analyzed by various accuracy metrics. The present study shows that the DTW and DTW-SVR are effective tools for bearing health degradation assessment and prognosis of rolling element bearing.