C
Craig A.S. Moodie
Researcher at University of Wollongong
Publications - 13
Citations - 400
Craig A.S. Moodie is an academic researcher from University of Wollongong. The author has contributed to research in topics: Condition monitoring & Slewing bearing. The author has an hindex of 7, co-authored 13 publications receiving 335 citations. Previous affiliations of Craig A.S. Moodie include Gyeongsang National University.
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Journal ArticleDOI
Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing
Wahyu Caesarendra,Wahyu Caesarendra,Buyung Kosasih,Anh Kiet Tieu,Hongtao Zhu,Craig A.S. Moodie,Qiang Zhu +6 more
TL;DR: In this paper, an acoustic emission-based method for the condition monitoring of low speed reversible slew bearings is presented, and several acoustic emission (AE) hit parameters as the monitoring parameters for the detection of impending failure of slew bearings are reviewed first.
Journal ArticleDOI
Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition
Wahyu Caesarendra,Wahyu Caesarendra,P.B. Kosasih,Anh Kiet Tieu,Craig A.S. Moodie,Byeong-Keun Choi +5 more
TL;DR: In this article, a very low rotational-speed slewing bearing (1-4.5 rpm) without artificial fault was used to detect outlier race fault and rolling element fault.
Journal ArticleDOI
Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring
TL;DR: In this paper, the largest Lyapunov exponent (LLE) algorithm is employed to measure the degree of nonlinearity of the vibration signal which is not easily monitored by existing methods.
Proceedings ArticleDOI
An application of nonlinear feature extraction - A case study for low speed slewing bearing condition monitoring and prognosis
TL;DR: Findings suggest that the largest Lyapunov exponent, fractal dimension, correlation dimension, and approximate entropy methods provide superior descriptive information about bearing condition than time-domain features extraction, such as root mean square, variance, skewness and kurtosis.
Journal ArticleDOI
Circular domain features based condition monitoring for low speed slewing bearing
TL;DR: In this article, a novel application of circular domain features calculation based condition monitoring method for low rotational speed slewing bearing is presented, which employs data reduction process using piecewise aggregate approximation (PAA) to detect frequency alteration in the bearing signal when the fault occurs.