Reliability modeling of complex mechanism system using GBN

Pidong Wang; Jianguo Zhang; Lechang Yang; Linjie Kan

doi:10.1109/RAMS.2015.7105087

Reliability modeling of complex mechanism system using GBN

Pidong Wang
, Jianguo Zhang
, Lechang Yang
, Linjie Kan

School of Reliability and Systems Engineering

Beihang University

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

Abstract

The Bayesian Network (BN) as a probability-based knowledge representation method is appropriate for modeling complex mechanism system reliability, when it is of interest in complex structures or multiple failure modes in the system. This paper presents a new Grey Bayesian Network (GBN) to solve the reliability problem for complex mechanism system with incomplete information and high uncertainty. In this new model, grey probability density functions (GPDF) of its nodes are obtained by grey generation theory and interval analyses instead of the ones represent random variables. The reliability of the complex mechanism is computed by the Monte Carlo simulation. Research on this method is performed by a space mechanism, and the results show the feasibility and validity of the proposed method.

Original language	English
Title of host publication	RAMS 2015 - 61st Annual Reliability and Maintainability Symposium, Proceedings and Tutorials 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479967025
DOIs	https://doi.org/10.1109/RAMS.2015.7105087
State	Published - 8 May 2015
Event	61st Annual Reliability and Maintainability Symposium, RAMS 2015 - Palm Harbor, United States Duration: 26 Jan 2015 → 29 Jan 2015

Publication series

Name	Proceedings - Annual Reliability and Maintainability Symposium
Volume	2015-May
ISSN (Print)	0149-144X

Conference

Conference	61st Annual Reliability and Maintainability Symposium, RAMS 2015
Country/Territory	United States
City	Palm Harbor
Period	26/01/15 → 29/01/15

Keywords

complex mechatronic system
grey
grey Bayesian network
grey probability density functions
partial information

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10.1109/RAMS.2015.7105087

Cite this

Wang, P., Zhang, J., Yang, L., & Kan, L. (2015). Reliability modeling of complex mechanism system using GBN. In RAMS 2015 - 61st Annual Reliability and Maintainability Symposium, Proceedings and Tutorials 2015 Article 7105087 (Proceedings - Annual Reliability and Maintainability Symposium; Vol. 2015-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAMS.2015.7105087

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title = "Reliability modeling of complex mechanism system using GBN",

abstract = "The Bayesian Network (BN) as a probability-based knowledge representation method is appropriate for modeling complex mechanism system reliability, when it is of interest in complex structures or multiple failure modes in the system. This paper presents a new Grey Bayesian Network (GBN) to solve the reliability problem for complex mechanism system with incomplete information and high uncertainty. In this new model, grey probability density functions (GPDF) of its nodes are obtained by grey generation theory and interval analyses instead of the ones represent random variables. The reliability of the complex mechanism is computed by the Monte Carlo simulation. Research on this method is performed by a space mechanism, and the results show the feasibility and validity of the proposed method.",

keywords = "complex mechatronic system, grey, grey Bayesian network, grey probability density functions, partial information",

author = "Pidong Wang and Jianguo Zhang and Lechang Yang and Linjie Kan",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 61st Annual Reliability and Maintainability Symposium, RAMS 2015 ; Conference date: 26-01-2015 Through 29-01-2015",

year = "2015",

month = may,

day = "8",

doi = "10.1109/RAMS.2015.7105087",

language = "英语",

series = "Proceedings - Annual Reliability and Maintainability Symposium",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "RAMS 2015 - 61st Annual Reliability and Maintainability Symposium, Proceedings and Tutorials 2015",

address = "美国",

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Wang, P, Zhang, J, Yang, L & Kan, L 2015, Reliability modeling of complex mechanism system using GBN. in RAMS 2015 - 61st Annual Reliability and Maintainability Symposium, Proceedings and Tutorials 2015., 7105087, Proceedings - Annual Reliability and Maintainability Symposium, vol. 2015-May, Institute of Electrical and Electronics Engineers Inc., 61st Annual Reliability and Maintainability Symposium, RAMS 2015, Palm Harbor, United States, 26/01/15. https://doi.org/10.1109/RAMS.2015.7105087

Reliability modeling of complex mechanism system using GBN. / Wang, Pidong; Zhang, Jianguo; Yang, Lechang et al.
RAMS 2015 - 61st Annual Reliability and Maintainability Symposium, Proceedings and Tutorials 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7105087 (Proceedings - Annual Reliability and Maintainability Symposium; Vol. 2015-May).

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

TY - GEN

T1 - Reliability modeling of complex mechanism system using GBN

AU - Wang, Pidong

AU - Zhang, Jianguo

AU - Yang, Lechang

AU - Kan, Linjie

PY - 2015/5/8

Y1 - 2015/5/8

N2 - The Bayesian Network (BN) as a probability-based knowledge representation method is appropriate for modeling complex mechanism system reliability, when it is of interest in complex structures or multiple failure modes in the system. This paper presents a new Grey Bayesian Network (GBN) to solve the reliability problem for complex mechanism system with incomplete information and high uncertainty. In this new model, grey probability density functions (GPDF) of its nodes are obtained by grey generation theory and interval analyses instead of the ones represent random variables. The reliability of the complex mechanism is computed by the Monte Carlo simulation. Research on this method is performed by a space mechanism, and the results show the feasibility and validity of the proposed method.

AB - The Bayesian Network (BN) as a probability-based knowledge representation method is appropriate for modeling complex mechanism system reliability, when it is of interest in complex structures or multiple failure modes in the system. This paper presents a new Grey Bayesian Network (GBN) to solve the reliability problem for complex mechanism system with incomplete information and high uncertainty. In this new model, grey probability density functions (GPDF) of its nodes are obtained by grey generation theory and interval analyses instead of the ones represent random variables. The reliability of the complex mechanism is computed by the Monte Carlo simulation. Research on this method is performed by a space mechanism, and the results show the feasibility and validity of the proposed method.

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KW - grey

KW - grey Bayesian network

KW - grey probability density functions

KW - partial information

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M3 - 会议稿件

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T3 - Proceedings - Annual Reliability and Maintainability Symposium

BT - RAMS 2015 - 61st Annual Reliability and Maintainability Symposium, Proceedings and Tutorials 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 61st Annual Reliability and Maintainability Symposium, RAMS 2015

Y2 - 26 January 2015 through 29 January 2015

ER -

Reliability modeling of complex mechanism system using GBN

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