Formal management-specifying approach for model-based safety assessment  

作　　者：XU Changyi DUAN Yiman ZHANG Chao 

机构地区：[1]School of Control Science and Engineering,Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian University of Technology,Dalian 116024,China [2]State Key Laboratory of Fluid Power and Mechatronic Systems,School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China

出　　处：《Journal of Systems Engineering and Electronics》2023年第6期1589-1601,共13页系统工程与电子技术（英文版）

基　　金：the National Natural Science Foundation of China(52105070,U21B2074);Department of Science and Technology of Liaoning Province China(2033JH1/10400007).

摘　　要：In the field of model-based system assessment,mathematical models are used to interpret the system behaviors.However,the industrial systems in this intelligent era will be more manageable.Various management operations will be dynamically set,and the system will be no longer static as it is initially designed.Thus,the static model generated by the traditional model-based safety assessment(MBSA)approach cannot be used to accurately assess the dependability.There mainly exists three problems.Complex:huge and complex behaviors make the modeling to be trivial manual;Dynamic:though there are thousands of states and transitions,the previous model must be resubmitted to assess whenever new management arrives;Unreusable:as for different systems,the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the same.Motivated by solving the above problems,this research studies a formal management specifying approach with the advantages of agility modeling,dynamic modeling,and specification design that can be re-suable.Finally,three typical managements are specified in a series-parallel system as a demonstration to show the potential.

关 键 词：model-based safety assessment(MBSA) MANAGEMENT AVAILABILITY RELIABILITY MAINTAINABILITY continuous time Markov chain. 

分 类 号：O213.2[理学—概率论与数理统计]