Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 184, Issue -, Pages 110-122Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2017.12.016
Keywords
Degradation-dependent weights; Remaining useful life; Prognostics; Ensemble learning; Locally weighted regression
Funding
- US National Science Foundation (NSF) [CNS-1566579, ECCS-1611333]
- U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R) through the Midwest Transportation Center (MTC)
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1566579] Funding Source: National Science Foundation
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1611333] Funding Source: National Science Foundation
Ask authors/readers for more resources
Remaining useful life (RUL) prediction is crucial for the implementation of predictive maintenance strategies. While significant research has been conducted in model-based and data-driven prognostics, there has been little research reported on the RUL prediction using an ensemble learning method that combines prediction results from multiple learning algorithms. The objective of this research is to introduce a new ensemble prognostics method that takes into account the effects of degradation on the accuracy of RUL prediction. Specifically, this method assigns an optimized, degradation-dependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RULs of engineered systems with better accuracy. The ensemble prognostics method is demonstrated using two case studies. One case study is to predict the RULs of aircraft bearings; the other is to predict the RULs of aircraft engines. The numerical results have shown that the predictive model trained by the ensemble learning-based prognostic approach with degradation-dependent weights is capable of outperforming the original ensemble learning-based approach and its member algorithms. (C) 2017 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available