Ensemble prognostics with degradation-dependent weights

Prediction of remaining useful life for aircraft engines

Zhixiong Li, Dazhong Wu, Chao Hu, Sheng Shen, Janis P. Terpenny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specifically, this method assigns an optimized, degradationdependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using a data set collected from an engine simulator. Analysis results show that the predictive model trained by the ensemble learning algorithm outperform the existing methods.

Original languageEnglish (US)
Title of host publication43rd Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791858127
DOIs
StatePublished - Jan 1 2017
EventASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017 - Cleveland, United States
Duration: Aug 6 2017Aug 9 2017

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2A-2017

Other

OtherASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017
CountryUnited States
CityCleveland
Period8/6/178/9/17

Fingerprint

Aircraft engines
Learning algorithms
Aircraft
Degradation
Ensemble
Engine
Dependent
Prediction
Learning Algorithm
Ensemble Learning
Simulators
Predictive Model
Weighted Sums
Engines
Assign
Simulator
Predict
Life

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

Cite this

Li, Z., Wu, D., Hu, C., Shen, S., & Terpenny, J. P. (2017). Ensemble prognostics with degradation-dependent weights: Prediction of remaining useful life for aircraft engines. In 43rd Design Automation Conference (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2A-2017). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2017-68315
Li, Zhixiong ; Wu, Dazhong ; Hu, Chao ; Shen, Sheng ; Terpenny, Janis P. / Ensemble prognostics with degradation-dependent weights : Prediction of remaining useful life for aircraft engines. 43rd Design Automation Conference. American Society of Mechanical Engineers (ASME), 2017. (Proceedings of the ASME Design Engineering Technical Conference).
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Li, Z, Wu, D, Hu, C, Shen, S & Terpenny, JP 2017, Ensemble prognostics with degradation-dependent weights: Prediction of remaining useful life for aircraft engines. in 43rd Design Automation Conference. Proceedings of the ASME Design Engineering Technical Conference, vol. 2A-2017, American Society of Mechanical Engineers (ASME), ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017, Cleveland, United States, 8/6/17. https://doi.org/10.1115/DETC2017-68315

Ensemble prognostics with degradation-dependent weights : Prediction of remaining useful life for aircraft engines. / Li, Zhixiong; Wu, Dazhong; Hu, Chao; Shen, Sheng; Terpenny, Janis P.

43rd Design Automation Conference. American Society of Mechanical Engineers (ASME), 2017. (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2A-2017).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Terpenny, Janis P.

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AB - The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specifically, this method assigns an optimized, degradationdependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using a data set collected from an engine simulator. Analysis results show that the predictive model trained by the ensemble learning algorithm outperform the existing methods.

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Li Z, Wu D, Hu C, Shen S, Terpenny JP. Ensemble prognostics with degradation-dependent weights: Prediction of remaining useful life for aircraft engines. In 43rd Design Automation Conference. American Society of Mechanical Engineers (ASME). 2017. (Proceedings of the ASME Design Engineering Technical Conference). https://doi.org/10.1115/DETC2017-68315