Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition

Authors

Wahyu Caesarendra, University of WollongongFollow
Prabuono Buyung Kosasih, University of WollongongFollow
Anh Kiet Tieu, University of WollongongFollow
Craig Alexander Simpson Moodie, University of WollongongFollow
Byeong-Keun Choi, Pukyong National University

RIS ID

82677

Publication Details

Caesarendra, W., Kosasih, P., Tieu, A. Kiet., Moodie, C. Alexander Simpson. & Choi, B. (2013). Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition. Journal of Mechanical Science and Technology, 27 (8), 2253-2262.

Abstract

There have been extensive studies on vibration based condition monitoring, prognosis of rotating element bearings; and reviews of the methods on how to identify bearing fault and predict the final failure reported widely in literature. The investigated bearings commonly discussed in the literatures were run in moderate and high rotating speed, and damages were artificially introduced e.g. with artificial crack or seeded defect. This paper deals with very low rotational-speed slewing bearing (1-4.5 rpm) without artificial fault. Two real vibration data were utilized, namely data collected from lab slewing bearing subject to accelerated life test and from a sheet metal company. Empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) were applied in both lab slewing bearing data and real case data. Outer race fault (BPFO) and rolling element fault (BSF) frequencies of slewing bearing can be identified. However, these fault frequencies could not be identified using fast Fourier transform (FFT).