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

Abstract

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.