International audienceRemaining Useful Life (RUL) estimation plays an important role in implementing a condition-based maintenance (CBM) program, since it could provide sufficient time for maintenance crew to act before an actual system failure. This prognostic task becomes harder when several deterioration mechanisms co-exist within the same system due to the variability and dynamics of its operating environment, since the RUL obviously depends on the mode that the system is following. In this paper, we propose a multi-branch modeling framework to deal with such problems. The proposed model consists of several branches in which each one represents a deterioration mode and is considered as a hidden Markov model. The system’s conditions are modeled by several discrete meaningful states, such as “good”, “minor defect”, “maintenance required” and “failure”, which would be easy to interpret for maintenance personnel. Furthermore, these states are considered to be “hidden” and can only be revealed through observations. These observations are the condition monitoring information in the CBM context. The performance of the proposed model is evaluated through numerical studies. The results show that the multi-branch model can outperform the standard one-branch HMM model in RUL estimation, especially when the “distance” between the deterioration modes is considerable

Deterioration modeling and remaining useful life (RUL) estimation of equipment are key enabling tasks for the implementation of a predictive maintenance (PM) policy, which plays nowadays an important role for maintaining engineering systems. Hidden Markov Models (HMM) have been used as an efficient tool for modeling the deterioration mechanisms as well as for estimating the RUL of monitored equipment. However, due to some assumptions not always justified in practice, the applications of HMM on real-life problems are still very limited. To tackle this issue and to relax some of these unrealistic assumptions, this paper proposes a multi-branch Hidden semi-Markov modeling (MB-HSMM) framework.The proposed deterioration model comprises several different branches, each one being itself an HSMM. The proposed model offers thus the capacity to 1) explicitly model the sojourn time in the different states and 2) take into account multiple co-existing and competing deterioration modes, even within a single component. A diagnosis and RUL prognosis methodology based on the MB-HSMM model is also proposed. Thanks to its multiple branches property, the MB-HSMM model makes it possible not only to assess the current health status of the component but also to detect the actual deterioration mechanism. Based on the diagnostic results, the component RUL can then be calculated.The performance of the proposed model and prognosis method is evaluated through a numerical study. A Fatigue Crack Growth (FCG) model based on the Paris-Erdogan law is used to simulate deterioration data of a bearing under different operation conditions. The results show that the proposed MB-HSMM gives a very promising performance in deterioration mode detection as well as in the RUL estimation, especially in the case where these deterioration modes exhibit very different dynamics.

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