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. To address this issue, we propose an online joint estimation and prediction methodology using a modeling framework based on the inoperability input-output model (IIM). This model can consider the interactions between components and also the mission profile effects on a system's degradation. To estimate the system's parameters in real-time, with a minimum of prior knowledge, an online estimation process based on the gradient descend algorithm is recursively performed when acquiring new measurements. After each update, the estimated model is used to predict the system RUL. The performance of the proposed approach is highlighted through different numerical examples. In addition, these developments are applied to a real industrial application, the Tennessee Eastman Process, in order to show their effectiveness.

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. Then, it combines these features to create the most appropriate HI following the previously defined multiple evaluation criteria. Thanks to the flexibility of the genetic programming, the proposed methodology does not require any expertise knowledge about system degradation trends but allows easily integrating this information if available. Its performance is then verified on two real application case studies. In addition, an insightful overview on HI evaluation criteria is also discussed in this paper.