Title of article :
An approximate algorithm for prognostic modelling using condition monitoring information
Author/Authors :
Matthew J. Carr، نويسنده , , Wenbin Wang، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Pages :
7
From page :
90
To page :
96
Abstract :
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.
Keywords :
Residual life , Condition based maintenance , Extended Kalman filter , Condition monitoring , Prognostic modelling
Journal title :
European Journal of Operational Research
Serial Year :
2011
Journal title :
European Journal of Operational Research
Record number :
1313168
Link To Document :
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