The role of NHPP models in the practical analysis of maintenance failure data

Abstract

The analysis of failure data is an important facet in the development of maintenance strategy for equipment. Only by properly understanding the mechanism of failure, through the modelling of failure data, can a proper maintenance plan be developed. This is normally done by means of probabilistic analysis of the failure data. From this, conclusions can be reached regarding the effectiveness and efficiency of preventive replacement (and overhaul) as well as that of predictive maintenance. The optimal frequency of maintenance can also be established by using well developed optimisation models. These optimise outputs, such as profit, cost and availability. The problem with this approach is that it assumes that all repairable systems are repaired to the 'good-as-new' condition at each repair occasion. Maintenance practice has learnt, however, that in many cases equipment slowly degrades even while being properly maintained (including part replacement and periodic overhaul). The result of this is that failure data sets often display degradation. This renders conventional probabilistic analysis useless. During the last two decades, a few researchers applied themselves to the solution of this problem. This paper briefly examines the present state of the theoretical foundation of repairable systems analysis techniques and then develops two formats of the Non-Homogeneous Poisson Process model (NHPP model) for practical use by the maintenance analyst. This includes an identification framework, goodness-of-fit tests and optimisation modelling. The model is tested on two failure data sets from literature and one from industry. © 1997 Elsevier Science Limited.