An approximate algorithm for prognostic modelling using condition monitoring information
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.
Year of publication: |
2011
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Authors: | Carr, Matthew J. ; Wang, Wenbin |
Published in: |
European Journal of Operational Research. - Elsevier, ISSN 0377-2217. - Vol. 211.2011, 1, p. 90-96
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Publisher: |
Elsevier |
Keywords: | Condition based maintenance Extended Kalman filter Condition monitoring Prognostic modelling Residual life |
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