A stochastic filtering based data driven approach for residual life prediction and condition based maintenance decision making support

As an efficient means of detecting potential plant failure, condition monitoring is growing popular in industry with million´s spent on condition monitoring hardware and software. The use of condition monitoring techniques will generally increase plant availability and reduce downtime costs, but in some cases it will also tend to over-maintaining the plant in question. There is obviously a need for appropriate decision support in plant maintenance planning utilising available condition monitoring information, but compared to the extensive literature on diagnosis, relatively little research has been done on the prognosis side of condition based maintenance. In plant prognosis, a key, but often uncertain quantity to be modelled is the residual life prediction based on available condition information to date. This paper shall focus upon such a residual life prediction of the monitored items in condition based maintenance and review the recent developments in modelling residual life prediction using stochastic filtering. We first demonstrate the role of residual life prediction in condition based maintenance decision making, which highlights the need for such a prediction. We then discuss in detail the basic filtering model we used for residual life prediction and the extensions we made. We finally present briefly the result of the comparative studies between the filtering based model and other models using empirical data. The results show that the filtering based approach is the best in terms of prediction accuracy and cost effectiveness.