Ensemble prognostics with degradation-dependent weights: Prediction of remaining useful life for aircraft engines

Zhixiong Li; Dazhong Wu; Chao Hu; Sheng Shen; Janis Terpenny

doi:10.1115/DETC2017-68315

Ensemble prognostics with degradation-dependent weights: Prediction of remaining useful life for aircraft engines

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

Marcus Department of Industrial and Manufacturing Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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 language	English (US)
Title of host publication	43rd Design Automation Conference
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791858127
DOIs	https://doi.org/10.1115/DETC2017-68315
State	Published - 2017
Event	ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017 - Cleveland, United States Duration: Aug 6 2017 → Aug 9 2017

Publication series

Name	Proceedings of the ASME Design Engineering Technical Conference
Volume	2A-2017

Other

Other	ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017
Country/Territory	United States
City	Cleveland
Period	8/6/17 → 8/9/17

All Science Journal Classification (ASJC) codes

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

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10.1115/DETC2017-68315

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Li, Z., Wu, D., Hu, C., Shen, S., & Terpenny, J. (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

@inproceedings{eca9303f88384adaa3204d00912b449f,

title = "Ensemble prognostics with degradation-dependent weights: Prediction of remaining useful life for aircraft engines",

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.",

author = "Zhixiong Li and Dazhong Wu and Chao Hu and Sheng Shen and Janis Terpenny",

note = "Publisher Copyright: {\textcopyright} Copyright 2017 ASME.; ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017 ; Conference date: 06-08-2017 Through 09-08-2017",

year = "2017",

doi = "10.1115/DETC2017-68315",

language = "English (US)",

series = "Proceedings of the ASME Design Engineering Technical Conference",

publisher = "American Society of Mechanical Engineers (ASME)",

booktitle = "43rd Design Automation Conference",

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Li, Z, Wu, D, Hu, C, Shen, S & Terpenny, J 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 et al.
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 proceeding › Conference contribution

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T1 - Ensemble prognostics with degradation-dependent weights

T2 - ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017

AU - Li, Zhixiong

AU - Wu, Dazhong

AU - Hu, Chao

AU - Shen, Sheng

AU - Terpenny, Janis

PY - 2017

Y1 - 2017

N2 - 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.

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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M3 - Conference contribution

AN - SCOPUS:85034742739

T3 - Proceedings of the ASME Design Engineering Technical Conference

BT - 43rd Design Automation Conference

PB - American Society of Mechanical Engineers (ASME)

Y2 - 6 August 2017 through 9 August 2017

ER -

Ensemble prognostics with degradation-dependent weights: Prediction of remaining useful life for aircraft engines

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