Reliability assessment of multi-state performance sharing systems with transmission loss and random shocks

Abstract

In this article, a performance sharing system with transmission loss and a shock operation environment is studied. Such systems are widely found in power distribution systems, distributed computing systems, data transmission systems, communication systems, and so on. The system consists of n components and each of them works to satisfy its demand and shares its performance surplus with others through a common bus. When the system operates, it may suffer a variety of stresses from its operating environment, which can be regarded as random external shocks, and the transmission loss is also wildly seen in engineering systems. Therefore, the random shocks and transmission loss are considered in this article. The performance level of a component is affected by three types of random external shocks – invalid shocks, valid shocks and extreme shocks. The system fails if at least one component cannot satisfy its demand. A finite Markov chain imbedding approach and phase-type distributions are used to estimate the performance level for each component and the universal generating function technique is applied to analyze system reliability. Analysis of a power distribution system is given to show the application of the model under study and the effectiveness of the proposed method.

Keywords:

• Performance sharing systems
• random external shocks
• universal generating function technique
• finite Markov chain imbedding approach
• phase-type distribution

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 72131002, 71971026, 71572014]. Congshan Wu was supported by the China Scholarship Council to study at Arizona State University as a visiting scholar from Sep. 2019 to Sep. 2020 (201906030032).

Notes on contributors

Congshan Wu

Congshan Wu received a bachelor’s degree from Hebei Normal University, China, in 2015, and a master’s degree from Beijing Institute of Technology, China, in 2018. She is currently pursuing her PhD degree in management science and engineering from Beijing Institute of Technology. Her research interests include reliability analysis and stochastic modelling.

Rong Pan

Rong Pan received his PhD degree in industrial engineering from Penn State University, State College, PA, USA, in 2002. He is currently an associate professor at the School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA. His research interests include quality and reliability engineering, design of experiments, time series analysis, and statistical learning theory. Dr. Pan is a Senior Member of ASQ, IISE and IEEE, and a Lifetime Member of SRE.

Xian Zhao

Xian Zhao received a Bachelor of Management degree in idustrial engineering in 2002 from Hebei University of Science and Technology, and the PhD degree in 2008 in management science and engineering from Beijing Institute of Technology. He is currently a Full Professor at the School of Management and Economics, Beijing Institute of Technology, China. His current research focuses on system reliability and management, and quality control.