Please use this identifier to cite or link to this item: https://doi.org/10.1080/0740817X.2012.706376
Title: Condition monitoring and remaining useful life prediction using degradation signals: Revisited
Authors: Chen, N. 
Tsui, K.L.
Keywords: Bayesian
Condition monitoring
degradation
remaining useful life
Issue Date: 1-Sep-2013
Source: Chen, N., Tsui, K.L. (2013-09-01). Condition monitoring and remaining useful life prediction using degradation signals: Revisited. IIE Transactions (Institute of Industrial Engineers) 45 (9) : 939-952. ScholarBank@NUS Repository. https://doi.org/10.1080/0740817X.2012.706376
Abstract: Condition monitoring is an important prognostic tool to determine the current operation status of a system/device and to estimate the distribution of the remaining useful life. This article proposes a two-phase model to characterize the degradation process of rotational bearings. A Bayesian framework is used to integrate historical data with up-to-date in situ observations of new working units to improve the degradation modeling and prediction. A new approach is developed to compute the distribution of the remaining useful life based on the degradation signals, which is more accurate compared with methods reported in the literature. Finally, extensive numerical results demonstrate that the proposed framework is effective and efficient. © 2013 Taylor & Francis Group, LLC.
Source Title: IIE Transactions (Institute of Industrial Engineers)
URI: http://scholarbank.nus.edu.sg/handle/10635/63063
ISSN: 0740817X
DOI: 10.1080/0740817X.2012.706376
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