Multi-criteria fuzzy model for system technical condition estimation at the life cycle stages

International Workshop "Advanced Technologies in Material Science, Mechanical and Automation Engineering – MIP: Engineering – 2019"

材料科学;机械制造;原子能学

Korshunov, G.I.^1^2 ; Smirnov, V.A.^3 ; Milova, V.M.^1

State Autonomous Educational Institution of Higher Education, Saint-Petersburg State University of Aerospace Instrumentation (SUAI), ul. BolshayaMorskaya lit. A 67, St. Petersburg

190000, Russia^1

Peter the Great St. Petersburg Polytechnic University (SPB STU), Polytechnicheskaya St. 29, Saint-Peterburg

195251, Russia^2

Closed Joint Stock Company Scientific-production Center Akvamarin, Tallinskaya St. 7, St. Petersburg

195196, Russia^3

关键词: Life cycle stages;    Life-cycle process;    Linguistic descriptions;    Linguistic variable;    Management decisions;    Qualitative estimation;    Quantitative estimation;    Technical conditions;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/537/4/042019/pdf
DOI  :  10.1088/1757-899X/537/4/042019

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

The article describes the tasks of in-depth estimation of the systems technical condition based on fuzzy ideas about the behavior of parameters within the tolerance limits. The proposed approach allows to more accurately control the influence of influencing factors at different stages of the life cycle of systems compared with the known methods. The approach is based on the application of six local criteria and a comprehensive criterion for assessing the level of system working capacity. The criteria are based on time series, linguistic variables, membership functions, and the proposed fuzzy classifier algorithm. Taking into account the peculiarities of the dynamics of changes in parameters in the tolerance field provides new opportunities for managing the life cycle processes of systems. The value of the evaluation of the level of working capacity is defined as the left most maximum of a fuzzy set for the corresponding output linguistic variable or the smallest of the modal values if the fuzzy set has several modal values. The quantitative estimation of working capacity is complemented by a qualitative estimation, expressed in the form of a linguistic description of the level of working capacity and degree of evaluation confidence in the result of recognition, understandable to the end user and convenient in making management decisions and developing recommendations at the stages of the life cycle.