A Copula-Based Multivariate Degradation Analysis for Reliability Prediction

Guanqi Fang; Rong Pan; Yili Hong

doi:10.1109/RAM.2018.8463026

A Copula-Based Multivariate Degradation Analysis for Reliability Prediction

Guanqi Fang, Rong Pan, Yili Hong

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

5 Scopus citations

Abstract

Degradation test often involves multivariate Performance Characteristics (PCs) to be analyzed to make reliability prediction. As a result, the complex dependency structure among PCs needs to be addressed. In this paper, we develop a flexible copula-based multivariate model for analyzing high-dimensional degradation process. A two-stage method for parameters estimation is developed as an efficient statistical inference scheme. Finally, a real LED dataset is analyzed by the proposed approach.

Original language	English (US)
Title of host publication	2018 Annual Reliability and Maintainability Symposium, RAMS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Volume	2018-January
ISBN (Print)	9781538628706
DOIs	https://doi.org/10.1109/RAM.2018.8463026
State	Published - Sep 11 2018
Event	2018 Annual Reliability and Maintainability Symposium, RAMS 2018 - Reno, United States Duration: Jan 22 2018 → Jan 25 2018

Other

Other	2018 Annual Reliability and Maintainability Symposium, RAMS 2018
Country/Territory	United States
City	Reno
Period	1/22/18 → 1/25/18

Keywords

copula function
degradation data analysis
multivariate degradation
reliability prediction

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
General Mathematics
Computer Science Applications

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10.1109/RAM.2018.8463026

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Fang, G, Pan, R & Hong, Y 2018, A Copula-Based Multivariate Degradation Analysis for Reliability Prediction. in 2018 Annual Reliability and Maintainability Symposium, RAMS 2018. vol. 2018-January, 8463026, Institute of Electrical and Electronics Engineers Inc., 2018 Annual Reliability and Maintainability Symposium, RAMS 2018, Reno, United States, 1/22/18. https://doi.org/10.1109/RAM.2018.8463026

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abstract = "Degradation test often involves multivariate Performance Characteristics (PCs) to be analyzed to make reliability prediction. As a result, the complex dependency structure among PCs needs to be addressed. In this paper, we develop a flexible copula-based multivariate model for analyzing high-dimensional degradation process. A two-stage method for parameters estimation is developed as an efficient statistical inference scheme. Finally, a real LED dataset is analyzed by the proposed approach.",

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N2 - Degradation test often involves multivariate Performance Characteristics (PCs) to be analyzed to make reliability prediction. As a result, the complex dependency structure among PCs needs to be addressed. In this paper, we develop a flexible copula-based multivariate model for analyzing high-dimensional degradation process. A two-stage method for parameters estimation is developed as an efficient statistical inference scheme. Finally, a real LED dataset is analyzed by the proposed approach.

AB - Degradation test often involves multivariate Performance Characteristics (PCs) to be analyzed to make reliability prediction. As a result, the complex dependency structure among PCs needs to be addressed. In this paper, we develop a flexible copula-based multivariate model for analyzing high-dimensional degradation process. A two-stage method for parameters estimation is developed as an efficient statistical inference scheme. Finally, a real LED dataset is analyzed by the proposed approach.

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KW - reliability prediction

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M3 - Conference contribution

AN - SCOPUS:85054130007

SN - 9781538628706

VL - 2018-January

BT - 2018 Annual Reliability and Maintainability Symposium, RAMS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 Annual Reliability and Maintainability Symposium, RAMS 2018

Y2 - 22 January 2018 through 25 January 2018

ER -

A Copula-Based Multivariate Degradation Analysis for Reliability Prediction

Abstract

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Keywords

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