A stochastic modelling development to system state prediction of high value, high risk systems subject to condition monitoring

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Condition monitoring is growing in popularity In industry with considerable sums now being spent on condition monitoring hardware and software. It is noted however that despite the significant rise In the profile of maintenance activities, and a burgeoning in the numbers and sophistication of condition monitoring equipment, systems continue to fail. Why is this? The single largest contributing factor Is that maintenance engineers lack a reliable way of prognosis. The aim of the project is to develop a modelling approach for fault detection, prognosis and subsequently maintenance decision making. The key technique we adopt Is what called a Hidden Markov Model (HMM) . It is a technique widely used in speech recognition and image segmentation.Here we assume the system monitored deteriorates according to a time/age dependent Markov process, but its state is unobservable. We furtherassume that the observed monitoring parameters is influenced by the underlying state of the system with random noise but not vice versus. A recursive filtering techniques is used to establish the initial fault detection and prognosis model based observed past history information. The model proposed will play a major role in condition based maintenance decision support, which in turn will save millions in UK industry if it proves to be valid.