Fault tree analysis method based on probabilistic model checking and discrete time Markov Chain

ABSTRACT

In order to fully consider the dynamic characteristics of event failure in fault tree analysis, the probabilistic model checking and discrete-time Markov chain are applied to the fault tree analysis. The formal probability of gate events in a fault tree is studied and the fault tree is established based on the probabilistic model checking technique. The logic gates in a fault tree are converted into discrete-time Markov chain that can be encoded into the probabilistic model checker. The discrete-time Markov chain is used to simulate the known behavior probability distribution of the system in the known situational environment, and the Markov decision process is used to simulate the non-deterministic behavior of the system under the unknown environment, and to carry out the quantitative analysis of the system. The fault tree analysis of the spindle system of a certain CNC machine tool is taken as an example to illustrate the proposed method.

KEYWORDS:

• Fault tree
• probabilistic model checking
• discrete-time Markov Chain
• Markov decision process
• spindle system of CNC machine tool

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was partially supported by the Natural Science Foundation of Jiangxi Province under the contract number [20181BAB202020], and the Young Scientists Object Program of Jiangxi Province, China under the contract number [20144BCB23037].

Notes on contributors

Ying-Kui Gu

Ying-Kui Gu is a professor in the School of Mechanical and Electronical Engineering at Jiangxi University of Science and Technology, China. He received his M.S. degree in Mechanical Engineering from the Jiangxi University of Science and Technology in 2002, and the Ph.D. degree in Mechanical Engineering from Dalian University of Technology, China, in 2005. His research interests include reliability engineering, fault diagnosis and optimization design.

Jun Zhang

Jun Zhang is currently a master degree candidate in Mechanical Engineering at Jiangxi University of Science and Technology. He received his B.S. degree in Mechanical Engineering from Henan University of Technology in 2016. His research interests include reliability engineering and fault diagnosis.

Yan-Jun Shen

Yan-Jun Shen is currently a master degree candidate in Mechanical Engineering at Jiangxi University of Science and Technology. He received his B.S. degree in Mechanical Engineering from China University of Geosciences in 2015. His research interests include reliability engineering and fault diagnosis.

Chao-Jun Fan

Chao-Jun Fan is currently a master degree candidate in Mechanical Engineering at Jiangxi University of Science and Technology. He received his B.S. degree in Mechanical Engineering from Jiangxi University of Science and Technology in 2015. His research interests include reliability engineering and fault diagnosis.