Prognostic considering missing data: An input output hidden Markov model based solution

The remaining useful life of a system is unknown and uncertain due to the uncertainty of system failure. However, by monitoring the behaviour of the system, it is possible to predict the current health and also the near future health states. To make a correct prognostic, we need to understand the degradation process of similar systems from the historical data, which is often not easy to collect in huge amount because the degradation process is a slow progression. A complete sequence requires collecting data from the beginning of a system’s operation until its death or failure. However, in reality, most deployments will have to deal with missing data, misreading or sensor saturation. This paper works on handling the missing data for improving the model training by extracting as much information as possible even from the incomplete sequences. In this paper, we propose an IOHMM-based missing data processing method, which is shown to provide better results compared to the list deletion method. A bootstrap method is developed that resamples using replacement sequences picked by the learning algorithms. Two well-known learning algorithms: the Baum Welch and the forward-backward algorithm are adapted to handle the missing data. A numerical application is simulated to demonstrate the role of the proposed algorithm and the corresponding model performance in RUL prediction, which is the basis of the RUL management.