A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability

Zhichao Feng; Wei He; Zhijie Zhou; Xiaojun Ban; Changhua Hu; Xiaoxia Han

doi:10.1109/JAS.2020.1003399

Volume 8 Issue 11

Nov. 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(11): 1774-1785

Z. C. Feng, W. He, Z. J. Zhou, X. J. Ban, C. H. Hu, and X. X. Han, "A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1774-1785, Nov. 2021. doi: 10.1109/JAS.2020.1003399

Citation:

Z. C. Feng, W. He, Z. J. Zhou, X. J. Ban, C. H. Hu, and X. X. Han, "A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1774-1785, Nov. 2021. doi: 10.1109/JAS.2020.1003399

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A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability

doi: 10.1109/JAS.2020.1003399

Zhichao Feng^{1, 3
,},
Wei He^{2
,
,},
Zhijie Zhou^1
,,
Xiaojun Ban^3
,,
Changhua Hu^1
,,
Xiaoxia Han^1
,

1.
Rocket Force University of Engineering, Xi’an 710025, China
2.
Harbin Normal University, Harbin 150080, China
3.
Harbin Institute of Technology, Harbin 150001, China

Funds: This work was supported in part by the National Natural Science Foundation of China (61833016, 61751304, 61873273, 61702142, 61773388), the Key Research and Development Plan of Hainan (ZDYF2019007), and Shaanxi Outstanding Youth Science Foundation (2020JC-34)

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Author Bio:
Zhichao Feng received the B.Eng. and M.Eng. degrees from the Rocket Force University of Engineering in 2016 and in 2018, respectively. He is currently pursuing the Ph.D. degree at Rocket Force University of Engineering and Harbin Institute of Technology. His research interests include belief rule base, evidential reasoning, information fusion, safety assessment, and fault prognosis and optimal maintenance of dynamic systems

Wei He received the B.Eng., M.Eng. and Ph.D. degrees at Harbin University of Science and Technology in 2004 to 2018, respectively. He is currently working at the School of Computer Science and Information Engineering, Harbin Normal University. His research interests include evidential reasoning, deep learning, and belief rule base

Zhijie Zhou received the B.Eng. and M.Eng. degrees from the Rocket Force University of Engineering in 2001 and 2004, respectively, and the Ph.D. degree from Tsinghua University in 2010, all in control science and management. He is currently an Associate Professor with the Rocket Force University of Engineering. In 2009, he was a Visiting Scholar with the University of Manchester, Manchester, UK, for six months. He has published approximately 70 articles. His research interests include belief rule base, dynamic system modeling, hybrid quantitative and qualitative decision modeling, and fault prognosis and optimal maintenance of dynamic systems

Xiaojun Ban received the B.Eng. and M.Eng. degrees from the Harbin Institute of Technology, in 2003 and in 2006, respectively. He is currently a Professor at the School of Astronautics, Harbin Institute of Technology, and Deputy Director of teaching at the Control Theory and Guidance Technology Research Center of the School of Astronautics, Harbin Institute of Technology. His research interests include reinforced learning control, fuzzy control theory and application, robust gain scheduling control theory and application, system identification theory and application, electromechanical motion servo control system design

Changhua Hu received the B.Eng. and M.Eng. degrees from the Rocket Force University of Engineering in 1987 and 1990, respectively, and the Ph.D. degree from North Western Polytechnic University in 1996, all in control science and management. He is currently a Professor with the Rocket Force University of Engineering. In 2008, he was a Visiting Scholar with the University of DuisburgEssen, Essen, Germany, for four months. He has published two books and approximately 100 articles. His current research interests include fault diagnosis and prediction, life prognosis, and fault-tolerant control

Xiaoxia Han received the B.Eng. and M.Eng. degrees from Xi’an University of Architecture and Technology, in 2001 and 2004, respectively. She is currently a Lecturer with the Rocket Force University of Engineering. Her current research interests include belief rule base and failure prognosis
Corresponding author: Wei He, e-mail: 64282426@qq.com
Received Date: 2020-07-07
Revised Date: 2020-07-22
Accepted Date: 2020-07-29

Available Online: 2020-08-11

Abstract

Abstract

Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.
- Belief rule base (BRB),
- belief rule reduction,
- reliability,
- safety assessment,
- structure adjustment

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Highlights

Safety assessment under environment interference
Complex system modeling based on observation data and expert knowledge
A new calculation method for belief rule reliability is developed where the input reliability and uncertain expert knowledge are both considered
A new optimization method to reduce the complexity of the belief rule base expert system and improve its modeling accuracy

A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability

doi: 10.1109/JAS.2020.1003399

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