A hybrid prognostic method based on gated recurrent unit network and an adaptive Wiener process model considering measurement errors

Remaining useful life (RUL) prediction is fundamental to prognostics and health management (PHM). Considering the advantages of both model-based and data-driven prognostic approaches, this paper develops a hybrid prognostic method for machinery degradation. First, a 3 alpha criterion-based algorithm is introduced to detect the initial timepoint of degradation. Second, gated recurrent unit (GRU) network is utilized to learn the degradation characteristics based on the available data and thereby predict the long-term degradation trend by a multi-prediction procedure. Then, an adaptive Wiener process model considering measurement errors is constructed. The states of this model consisting of the drift rate and the underlying degradation value are updated adaptively based on the monitored observations and the predictions by GRU using a Kalman filtering algorithm. The predicted values of the RUL can be determined according to the underlying degradation and the failure threshold. Finally, to account for the drift adaptivity in the future degradation, exponentially weighted average method is adopted to aggregate the estimated drift sequence from the current time until failure for the derivation of real-time RUL distributions. The effectiveness and superiority are illustrated by a simulation study and an application to rolling element bearings. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper develops a hybrid prognostic method for machinery degradation, combining model-based and data-driven approaches. The method utilizes GRU network to learn degradation characteristics and adaptively updates a Wiener process model. The effectiveness of the method is illustrated through simulation studies and application to rolling element bearings.