An approximate algorithm for prognostic modelling using condition monitoring information

Matthew Carr, Wenbin Wang

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)90-96
JournalEuropean Journal of Operational Research
Issue number1
Publication statusPublished - 2011


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