Using hidden Markov models for fault diagnostics and prognosis in condition based maintenance systems
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Condition Based Maintenance (CBM)) is a concept that has become more and more important as the cost, size, and complexity of mechanical components has increased. As more sensor equipment has become available it has become possible to measure different kinds of status data, such as vibration, temperature, or electric current, from different kinds of mechanical components. By using this status data it should be possible to determine the health of mechanical components, and determine when they need maintenance, and what parts needs to be replaced. Hidden Markov models (HMM) is a statistical model for modelling systems that evolve through a finite number of states. By using HMMs it is possible to detect and recognize different kinds of anomalies and errors present in the system. It is also possible to estimate when the system is going to be in a state where it can not be expected that the system is going to function properly (error state). During this thesis I have examined several different ways of applying HMMs to tasks related to condition based maintenance. HMMs have been tested in their use in anomaly detection, current state detection, and future state prediction. All these tasks have been performed with little available training data to demonstrate how HMMs can still be used even if little prior knowledge about the system is available. Tests have been performed to evaluate how well HMMs can detect unknown data (anomaly detection), determine the current health of the system (current state detection), and predict the future health of the system (future state prediction). During all the tests it has also been a focus on how limited training data and expert knowledge about the system will influence the approach to the problem and the results of the tests. Out of all the test that were performed anomaly detection is the task that was least influenced by the lack of training data or prior knowledge. Current state detection accuracy was most influenced by the lack of prior knowledge and the quality of the training data, while future state prediction could benefit from more training data. Based on the test it is possible to say that anomaly detection can be performed with just a minimum of training data. However, prior knowledge about the system becomes more important because anomalies needs to be classified. Current state detection requires more training data than anomaly detection. Prior knowledge about the system is also important when training HMMs to recognize error states. Future state prediction requires a lot of varied training data to be able to perform reasonably accurate predictions for new components, that may have a longer life-cycle than previously seen components. The results of these tests gives an indication about the requirements for training data, and knowledge about the system where anomaly detection, current state detection, and future state prediction is being performed.
Masteroppgave i informasjons- og kommunikasjonsteknologi 2010 – Universitetet i Agder, Grimstad