Section: New Results

Detection of a damaged operating mode of optronic equipment using Hidden Markov Model

Participants : Camille Baysse, Anne Gégout-Petit, Jérôme Saracco.

As part of optimisation of the reliability, Thales Optronics now includes systems that examine the state of its equipment. This function is performed by HUMS ( Health & Usage Monitoring System). The aim is to implement a program based on these observations that can determine the lifetime of this optronic equipment and optimize its maintenance.

Our study focuses on a simple example of HUMS. As part of our research, we are interested in a variable called "time-to cold" noted TMF, which reflects the state of the system. Using these informations about this variable, we seek to detect as soon as possible a damaged state and propose a maintenance before failure. This would allow the Thales Optronics company to improve its maintenance system and achieve many economies.

For this we use a hidden Markov model. The state of our system at time t is then modeled by a continuous time Markov chain X(t) with three states : stable, damaged and failure. However we do not observe directly this chain but indirectly through the TMF, a noisy function of this chain. Thanks to filtering equations, we obtained results on the probability that an equipment is in a damaged state at time t, knowing the history of the TMF until this moment. We have subsequently studied the method on simulated data., before applying these results on the analysis of our real data and we have checked that the results are consistent with the reality. This work will be used by Thales Optronique for the optimization of the maintenance.

This work is a part of the CIFRE PhD Thesis of Camille Baysse founded by Thales Optronic. It is the object of a technical report [48] and was presented in [29] in an internal Thales seminar.