  As part of optimizing the reliability, Thales Optronics now includes systems
that examine the state of its equipment. The aim of this paper is to use hidden
Markov Model to detect as soon as possible a change of state of optronic
equipment in order to propose maintenance before failure. For this, we
carefully observe the dynamic of a variable called "cool down time" and noted
Tmf, which reflects the state of the cooling system. Indeed, the Tmf is an
indirect observation of the hidden state of the system. This one is modelled by
a Markov chain and the Tmf is a noisy function of it. Thanks to filtering
equations, we obtain results on the probability that an appliance is in
degraded state at time $t$, knowing the history of the Tmf until this moment.
We have evaluated the numerical behavior of our approach on simulated data.
Then we have applied this methodology on our real data and we have checked that
the results are consistent with the reality. This method can be implemented in
a HUMS (Health and Usage Monitoring System). This simple example of HUMS would
allow the Thales Optronics Company to improve its maintenance system. This
company will be able to recall appliances which are estimated to be in degraded
state and do not control to soon those estimated in stable state.
