Failure Event Prediction Using Hidden Markov Model Approaches

In the past years, Hidden Markov Models have been used in several fields and applications with success. More recently, these models have been applied to improve the reliability of a machinery system. In many cases, failure is preceded by specific sequences of events (signature), which can be detected by an adequate Hidden Markov Model (HMM). Classical laws like lifetime models or survival functions are used to estimate the lifetime of a system. The default of these laws is that only the elapsed time is used to estimate the end of life of a system. The aim of this paper is to validate an HMM approach. We first use a synthetic HMM model of degradation to produce event sequences. This synthetic model has been inspired by a real process. In this case, we can adjust the failure rate by changing model parameters. All the parameters of this synthetic model are known and provide references which can be evaluated by different indicators. Classical survival functions used in reliability are computed on synthetic sequences. These laws validate the behavior of the synthetic model. The higher the failure rate, the shorter the lifetime duration. These results confirm that a four-state, left to right, HMM topology can represent the degradation level of a system. In a second time, this HMM approach is used in a real case, where degradation levels are unknown. Degradation estimates are compared with the results from classical survival functions used in the first case. Then we show that the degradation level provided by the HMM approach is more efficient than the survival functions approach. The HMM approach takes into account the events collected about a system, not only the elapsed time as is the case with survival functions.