Ensemble Learning for Remaining Useful Life Prediction
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Jul 14, 2017
Zhixiong Li
Chao Hu
Abstract
While significant research has been conducted in modelbased and data-driven prognostics, very limited research has been done to investigate the prediction of RUL using an ensemble learning method that combines prediction results from multiple learning algorithms. This research aims to introduce a new ensemble prognostics method with degradation-dependent weights. The performance of the proposed method is evaluated by the C-MAPSS data sets.
##plugins.themes.bootstrap3.article.details##
Keywords
PHM
References
1. Hu, C., Youn, B., Wang, P., & Yoon, J. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering and System Safety, vol. 103, pp. 120–135. doi: 10.1016/j.ress.2012.03.008
2. Goebel, K., Saha, B., & Saxena, A. (2008). A Comparison of Three Data-Driven Techniques for Prognostics. Proceedings of MFPT 62. May 6-8, Virginia Beach, VA.
3. Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008) Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation. Proceedings of Prognostics and Health Management 2008. Oct 6-9, Dever, CO. doi: 10.1109/PHM.2008.4711414
2. Goebel, K., Saha, B., & Saxena, A. (2008). A Comparison of Three Data-Driven Techniques for Prognostics. Proceedings of MFPT 62. May 6-8, Virginia Beach, VA.
3. Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008) Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation. Proceedings of Prognostics and Health Management 2008. Oct 6-9, Dever, CO. doi: 10.1109/PHM.2008.4711414
Section
Invited Papers