Multi-state reliability analysis and assessment for complex technical system under semi-Markov processes

被引：0

作者：

Shi Y. ^{[1
]}

Jin J. ^{[1
]}

Xu Y. ^{[2
]}

机构：

[1] College of Naval Architecture and Marine, Naval University of Engineering, Wuhan

[2] Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2019年 / 41卷 / 02期

关键词：

Analysis and assessment; Multi-state; Reliability; Semi-Markov processes; System of systems;

D O I：

10.3969/j.issn.1001-506X.2019.02.30

中图分类号：

学科分类号：

摘要：

Developing the reliability modeling, analysis and assessment technology with respect to multi-state complex equipment system plays an important role for reducing risk in development, optimizing construction of support system, and improving efficiency in use. The regular reliability analysis and assessment system use Markov processes to deconstruct multi-state transition. However, the modeling subjects to limitations on precision and credibility. Breaking the exponential distribution hypothesis, a new system to model, analyse and assess multi-state reliability using semi-Markov processes is established and applied to assess multi-state reliability about the strong electromechanical coupling marine generator. Research shows that the new solution system can effectively promote assessment precision and data credibility for multi-state complex technical system with the characteristic of strong electromechanical coupling, can effectively avoid risks during the design and the usage phases, and can make more rational decisions for the support system. Moreover, the results have an important value in theoretical analysis and engineering application for the designers and the users. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：444 / 452

页数：8

共 45 条

[1]

Anatoly L., Ilia F., Yi D., Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers, (2010)

[2]

Krzysztof K., Joanna S.B., Reliability and Safety of Complex Technical Systems and Processes, (2011)

[3]

Amari S.V., Xing L., Shretha A., Et al., Performability analysis of multi-state computing systems using multivalued decision diagram, IEEE Trans. on Computers, 59, 10, pp. 1419-1433, (2010)

[4]

Xing L., Dai Y.S., A new decision-diagram-based method for efficient analysis on multi-state systems, IEEE Trans. on Dependable and Secure Computing, 6, 3, pp. 161-174, (2009)

[5]

Shretha A., Xing L., Dai Y.S., Decision diagram based methods and complexity analysis for multi-state systems, IEEE Trans. on Reliability, 59, 1, pp. 145-161, (2010)

[6]

Qian W.X., Yin X.W., Xie L.Y., Discretized modeling process of reliability of multi-state mechanical systems, Journal of Northeastern University, 29, 11, pp. 1609-1612, (2008)

[7]

Zhou Z.B., Ma C.Q., Zhou J.L., Et al., Multi-state fault tree analysis method based on Bayesian networks, Mathematics in Practice and Theory, 38, 19, pp. 89-95, (2008)

[8]

Yin X.W., Qian W.X., Xie L.Y., Multi-state system reliability modeling and assessment based on Bayesian networks, Journal of Mechanical Engineering, 45, 2, pp. 206-212, (2009)

[9]

Yamanoto H., Zuo M.J., Akibat, Et al., Recursive formulas for the reliability of multi-state consecutive-k-out-of-n: G systems, IEEE Trans. on Reliability, 55, 1, pp. 98-104, (2006)

[10]

Wu Y.Q., Zhou H., Guan J.C., Reliability estimation of a multi-state system, Journal of Beijing University of Aeronautics and Astronautics, 33, 8, pp. 968-971, (2007)

← 1 2 3 4 5 →