Prognostics Estimations with Dynamic States

TL;DR

This paper introduces a unified prognostics method for aero-engines that simultaneously predicts continuous health states and discrete operating states dynamically, addressing the challenge of unknown failure thresholds in uncertain environments.

Contribution

It proposes a novel joint prediction framework that estimates both continuous and discrete states simultaneously, simplifying the learning process for dynamic machinery prognostics.

Findings

Effective joint prediction of states demonstrated

Reduces complexity compared to separate algorithms

Improves RUL estimation accuracy

Abstract

The health state assessment and remaining useful life (RUL) estimation play very important roles in prognostics and health management (PHM), owing to their abilities to reduce the maintenance and improve the safety of machines or equipment. However, they generally suffer from this problem of lacking prior knowledge to pre-define the exact failure thresholds for a machinery operating in a dynamic environment with a high level of uncertainty. In this case, dynamic thresholds depicted by the discrete states is a very attractive way to estimate the RUL of a dynamic machinery. Currently, there are only very few works considering the dynamic thresholds, and these studies adopted different algorithms to determine the discrete states and predict the continuous states separately, which largely increases the complexity of the learning process. In this paper, we propose a novel prognostics approach for RUL estimation of aero-engines with self-joint prediction of continuous and discrete states, wherein the prediction of continuous and discrete states are conducted simultaneously and dynamically within one learning framework.