Computation of remaining useful life on a physic-based model and impact of a prognosis on the maintenance process

Abstract

The aim of this article is double: propose a methodology for a probabilistic prognosis, and examine how the prognosis result impacts the maintenance process. First, the prognosis problem is mathematically defined: it consists in computing the distribution of the remaining useful life of the system conditionally to on-line available information. Considering on-line information allows to provide a specific prognosis for each system according to its life. Second, a global methodology is proposed when the state of the system and its degradations are modeled using a Markov process. This method is basically a two-step technique. On one hand, it requires the computation of the conditional law of the system regarding the available observations. On the other hand, it involves the computation of the reliability of the system. Some reliability computation techniques are proposed when the Markov process is a piecewise deterministic Markov process. The method is illustrated on an aeronautic example: a pneumatic valve within the bleed air system, used to provide regulated air (pressure, temperature) in the cabin. Eventually, the prognosis result is used to help maintenance optimization on an illustrative example. It highlights that the prognosis mainly improves the maintenance decision if the on-line available information is accurate enough.

Keywords

Prognosis piecewise deterministic Markov process particle filtering dynamic reliability

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