Testing and comparing the application of new techniques to condition based maintenance

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The objective of the proposed research is to introduce and test new techniques for monitoring, diagnosis and prognosis of machine health based on condition monitoring, to compare the performance of those techniques, and to optimize maintenance actions accordingly. The first technique is a combination of the Proportional Hazards Model (PHM) and a Partially Observable Markov Decision Model (POMDM), two models which are frequently mentioned in the literature, but which are used separately. The second technique is based on a Statistical Process Control (SPC) chart, the Hotelling's  T2 . The third technique uses the Support Vector Machines (SVM) model, which was originally developed for solving pattern recognition problems. The fourth technique applies the Logical Analysis of Data (LAD), a combinatorics and optimization-based data analysis method.These techniques represent four different approaches to CBM based on modeling degradation, outlier detection, regression analysis, and group classification, respectively.The scientific approach is to model and then compare the performance of those four techniques based on the following key elements of CBM:1.The detection of trends or patterns in the monitored indicators;2.The prediction of the machine's future health based on the remaining useful life or the probability of failure;3. The interface of an optimization tool or a maintenance decision-making process with those techniques.The novelty of this work is twofold:1.Three of the four approaches have never been used for prediction or with an optimization tool in condition based maintenance, although success in applying these techniques for diagnosis has been reported. It is expected that those techniques will perform well in CBM and thus new techniques will be available to interested practitionners.2.A comparative study of different techniques used in CBM has never been reported. It is expected that such a study will help practitionner identify the most suitable techniques for their needs.