Prognostics Estimations with Dynamic States
This work addresses prognostics and health management for machinery like aero-engines, offering an incremental improvement by integrating state predictions to handle dynamic thresholds.
The paper tackles the problem of remaining useful life (RUL) estimation for aero-engines in dynamic environments by proposing a novel prognostics approach that jointly predicts continuous and discrete states within a single learning framework, aiming to reduce complexity compared to existing methods.
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.