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Machines 2017, 5(2), 11; doi:10.3390/machines5020011

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

1
Tribology Laboratory, School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, New South Wales 2522, Australia
2
Mechanical Engineering Department, Diponegoro University, Tembalang, Semarang 50275, Indonesia
3
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Hongrui Cao
Received: 24 February 2017 / Revised: 17 March 2017 / Accepted: 28 March 2017 / Published: 4 April 2017
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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. View Full-Text
Keywords: condition monitoring; kernel regression; low speed slew bearing; multivariate state estimation technique; prognosis; sequential probability ratio test condition monitoring; kernel regression; low speed slew bearing; multivariate state estimation technique; prognosis; sequential probability ratio test
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Caesarendra, W.; Tjahjowidodo, T.; Kosasih, B.; Tieu, A.K. Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings. Machines 2017, 5, 11.

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