Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 211, Issue 1, Pages 90-96Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2010.10.023
Keywords
Condition based maintenance; Extended Kalman filter; Condition monitoring; Prognostic modelling; Residual life
Funding
- Research Grants Council of the Hong Kong Special Administrative Region, China [CityU8/CRF/09]
- National Natural Science Foundation of China [71071097]
- EPSRC [EP/F038526/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/F038526/1] Funding Source: researchfish
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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. (C) 2010 Elsevier B.V. All rights reserved.
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