Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings

Authors

Wahyu Caesarendra, University of WollongongFollow
Tegoeh Tjahjowidodo, Nanyang Technological University
Buyung Kosasih, University of WollongongFollow
Anh Kiet Tieu, University of WollongongFollow

RIS ID

113373

Publication Details

Caesarendra, W., Tjahjowidodo, T., Kosasih, B. & Tieu, A. K. (2017). Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings. Machines, 5 (2), 5020011-1-5020011-20.

Abstract

This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps. Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature. Step (2) is the prediction of the selected features' trends and the estimation of the remaining useful life (RUL) of the slew bearing. In this step, kernel regression is used with time-domain kurtosis, WD kurtosis and the LLE feature. The application of the method is demonstrated with laboratory slew bearing acceleration data.