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...

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Vydáno v:	European journal of operational research Ročník 211; číslo 1; s. 90 - 96
Hlavní autoři:	Carr, Matthew J., Wang, Wenbin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 16.05.2011 Elsevier Elsevier Sequoia S.A
Edice:	European Journal of Operational Research
Témata:	Applied sciences Approximation Comparative analysis Condition based maintenance Condition based maintenance Extended Kalman filter Condition monitoring Prognostic modelling Residual life Condition monitoring Decision making models Decision theory. Utility theory Exact sciences and technology Extended Kalman filter Industrial metrology. Testing Information Kalman filters Maintenance Mathematical analysis Mathematical models Mechanical engineering. Machine design Methodology Modelling Operational research and scientific management Operational research. Management science Prognostic modelling Reliability theory. Replacement problems Residual life Studies Condition monitoring EKF MLE CM RL AIC Condition based maintenance Residual life Extended Kalman filter Prognostic modelling CBM Script Data analysis Automation On line Vibration Kalman filter Replacement problem Durability Inference Maintenance Conditional probability Modeling Multidimensional analysis Surveillance Probability density function Kalman filtering Monitoring
ISSN:	0377-2217, 1872-6860
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Popis
Shrnutí:	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.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0377-2217 1872-6860
DOI:	10.1016/j.ejor.2010.10.023