An approximate algorithm for prognostic modelling using condition monitoring information

作者：Carr, Matthew J.; Wang, Wenbin^*

来源：European Journal of Operational Research, 2011, 211(1): 90-96.

DOI：10.1016/j.ejor.2010.10.023

• Condition based maintenance
• Extended Kalman filter
• Condition monitoring
• Prognostic modelling
• Residual life

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.

• 出版日期2011-5-16
• 单位香港城市大学