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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Veröffentlicht in:	European journal of operational research Jg. 211; H. 1; S. 90 - 96
Hauptverfasser:	Carr, Matthew J., Wang, Wenbin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 16.05.2011 Elsevier Elsevier Sequoia S.A
Schriftenreihe:	European Journal of Operational Research
Schlagworte:	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
Online-Zugang:	Volltext
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