Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects.

Failure prognostics has become a central element in predictive maintenance. In this domain, the accurate determination of the remaining useful life (RUL) allows making effective maintenance and operation decisions about the assets. However, prognostics is often approached from a component point of view, and system-level prognostics, taking into account component interactions and mission profile effects, is still an underexplored area.

An automated health indicator construction methodology for prognostics based on multi-criteria optimization.

In recent years, the development of autonomous health management systems received increasing attention from worldwide companies to improve their performances and avoid downtime losses. This can be done, in the first step, by constructing powerful health indicators (HI) from intelligent sensors for system monitoring and for making maintenance decisions. In this context, this paper aims to develop a new methodology that allows automatically choosing the pertinent measurements among various sources and also handling raw data from high-frequency sensors to extract the useful low-level features.