A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System

Jun Peng,Shengnan Wang,Bin Chen,Wentao Yu,Dianzhu Gao,Zhiwu Huang,Yingze Yang,Xiaoyong Zhang,Yijun Cheng

doi:10.3390/app10041378

Abstract

Engineering systems typically go through complicated degradation processes, partially due to their multiple operating modes. Therefore, how to accurately estimate their remaining useful life is a critical issue. To address this challenge, a hybrid degradation modeling and prognostic method for the multi-modal system is proposed. Firstly, the cumulative dynamic differential health indicator is constructed for the multi-modal switching system using a multi-objective optimization approach. The long-term cumulative degradation assessment model is constructed based on the gated recurrent unit. Then, considering that the damage in the latest stage has a significant impact on the remaining useful life, the time window is used to extract local features of the sequence, including energy features and statistical features. The latest stage degradation is predicted based on the light gradient boosting machine. Finally, model averaging is used to integrate the two predicted results, which is expected to improve the prognostic robustness. The proposed model is evaluated with synthetic analysis and NASA turbofan aero-engine datasets. Extensive experimental results demonstrate the proposed method provides a better characterization of the degradation status of the system and provides a higher estimation accuracy than existing methods.

Highlights

Prognostic health management is a critical piece of technology for providing early warnings of system failure [1], aiming to minimize economic loss and potentially catastrophic accidents
The remaining useful life is set as the percentage of useful times
Considering that late predictions have more serious consequences, two evaluation metrics, the root mean squared error (RMSE) and scores are used to evaluate the performance of the proposed method

Summary

Introduction

Prognostic health management is a critical piece of technology for providing early warnings of system failure [1], aiming to minimize economic loss and potentially catastrophic accidents. As a vital issue of prognostic health management, the remaining useful life (RUL), which is defined as the duration from the current time to the failure time, should be predicted accurately. An engineering system can work under a single operating condition or multiple operating conditions depending the application scenario. Most researchers concentrate on the prognostic health management of system with the single operation condition [2,3,4,5,6,7,8,9,10]. In practice, the engineering system often works under multiple operating conditions [11] and switches operation modes according to the changes in the operating environment and its states.

Results

Discussion

Conclusion