An approximate algorithm for prognostic modelling using condition monitoring information

Matthew J. Carr and Wenbin Wang

European Journal of Operational Research, 2011, vol. 211, issue 1, 90-96

Abstract: Established condition based maintenance modelling techniques can be computationally expensive. In this paper we propose an approximate methodology using extended Kalman-filtering and condition monitoring information to recursively establish a conditional probability density function for the residual life of a component. The conditional density is then used in the construction of a maintenance/replacement decision model. The advantages of the methodology, when compared with alternative approaches, are the direct use of the often multi-dimensional condition monitoring data and the on-line automation opportunity provided by the computational efficiency of the model that potentially enables the simultaneous condition monitoring and associated inference for a large number of components and monitored variables. The methodology is applied to a vibration monitoring scenario and compared with alternative models using the case data.

Keywords: Condition; based; maintenance; Extended; Kalman; filter; Condition; monitoring; Prognostic; modelling; Residual; life (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:211:y:2011:i:1:p:90-96

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