A PHM System Approach: Application to a Simplified Aircraft Bleed System

Regarding Prognostics and Health Management (PHM), the stakes lie in system-level prognostics or even the prognostics of systems of systems, as decisions are usually made at system or platform level. In this paper, a method, which takes into account both the system redundancy and the adaptation of operational modes in degraded functioning, is proposed and formalized. This method makes the system-level prognostics more relevant. The main feature of the method is to re-compute the components Remaining Useful Life (RUL) using the degradation rate associated to the future operating mode(s) due to system reconfiguration. This results in an improvement of both the System (SRUL) and the components . The proposed method is applied on a simplified aircraft bleed valve system to illustrate its effectiveness. This method is primarily destined to aeronautic systems, which are usually resilient. It has not been tested whether or not it could be useful in other fields

Random Forests for Industrial Device Functioning Diagnostics Using Wireless Sensor Networks

International audienceIn this paper, random forests are proposed for operating devices diagnostics in the presence of a variable number of features. In various contexts, like large or difficult-to-access monitored areas, wired sensor networks providing features to achieve diagnostics are either very costly to use or totally impossible to spread out. Using a wireless sensor network can solve this problem, but this latter is more subjected to flaws.Furthermore, the networks’ topology often changes, leading to a variability in quality of coverage in the targeted area.Diagnostics at the sink level must take into consideration that both the number and the quality of the provided features are not constant, and that some politics like scheduling or data aggregation may be developed across the network. The aim of this article is (1) to show that random forests are relevant in this context, due to their flexibility and robustness, and (2) to provide first examples of use of this method for diagnostics based on data provided by a wireless sensor network