Latent degradation indicators estimation and prediction: a Monte Carlo approach

Zhou, Yifan, Sun, Yong, Mathew, Joseph, Wolff, Rodney and Ma, Lin (2011) Latent degradation indicators estimation and prediction: a Monte Carlo approach. Mechanical Systems and Signal Processing, 25 1: 222-236. doi:10.1016/j.ymssp.2010.08.012

Author Zhou, Yifan
Sun, Yong
Mathew, Joseph
Wolff, Rodney
Ma, Lin
Title Latent degradation indicators estimation and prediction: a Monte Carlo approach
Journal name Mechanical Systems and Signal Processing   Check publisher's open access policy
ISSN 0888-3270
Publication date 2011-01-01
Sub-type Article (original research)
DOI 10.1016/j.ymssp.2010.08.012
Open Access Status
Volume 25
Issue 1
Start page 222
End page 236
Total pages 15
Place of publication London, United Kingdom
Publisher Academic Press
Language eng
Abstract Asset health inspections can produce two types of indicators: (1) direct indicators (e.g. the thickness of a brake pad, and the crack depth on a gear) which directly relate to a failure mechanism; and (2) indirect indicators (e.g. the indicators extracted from vibration signals and oil analysis data) which can only partially reveal a failure mechanism. While direct indicators enable more precise references to asset health condition, they are often more difficult to obtain than indirect indicators. The state space model provides an efficient approach to estimating direct indicators by using indirect indicators. However, existing state space models to estimate direct indicators largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires fixed inspection intervals. The discrete state assumption entails discretising continuous degradation indicators, which often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This paper proposes a state space model without these assumptions. Monte Carlo-based algorithms are developed to estimate the model parameters and the remaining useful life. These algorithms are evaluated for performance using numerical simulations through MATLAB. The result shows that both the parameters and the remaining useful life are estimated accurately. Finally, the new state space model is used to process vibration and crack depth data from an accelerated test of a gearbox. During this application, the new state space model shows a better fitness result than the state space model with linear and Gaussian assumption.
Keyword Degradation model
EM algorithm
Particle filter
Particle smoother
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: W.H. Bryan Mining Geology Research Centre
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Citation counts: TR Web of Science Citation Count  Cited 17 times in Thomson Reuters Web of Science Article | Citations
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Created: Wed, 18 Jun 2014, 00:57:04 EST by Rodney Wolff on behalf of WH Bryan Mining and Geology Centre