Overview of a semi-stochastic filtering approach for residual life estimation with applications in condition based maintenance

Maintenance policies include break-down-based maintenance, time-based maintenance, and condition-based maintenance. The advances in condition monitoring techniques have made condition-based maintenance a popular and increasingly important choice. With the increased use of condition monitoring information, there is obviously a need for appropriate decision support in plant maintenance planning utilizing available condition monitoring information. However, compared with 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 overviews a semi-stochastic filtering-based residual life prediction approach for the monitored items in condition-based maintenance and introduces the associated applications. First the role of residual life prediction in condition-based maintenance decision making is demonstrated, which highlights the need for such a prediction. Then a detailed discussion is presented of the semi-stochastic filtering models developed for residual life prediction, the extensions made, and the case applications applied to. Finally the results of a comparative study between the semi-stochastic filtering based model and another popular model using empirical data are briefly given. The results show that the filtering-based approach is better in terms of prediction accuracy and cost effectiveness.