The Improvement of maintainability evaluation method at system level using system component information and fuzzy technique
Author(s) -
Yeon-Yong Yoo,
Jae-Chon Lee
Publication year - 2019
Publication title -
journal of the korea academia-industrial cooperation society
Language(s) - English
Resource type - Journals
eISSN - 2288-4688
pISSN - 1975-4701
DOI - 10.5762/kais.2019.20.3.100
Subject(s) - maintainability , component (thermodynamics) , fuzzy logic , computer science , reliability engineering , data mining , artificial intelligence , engineering , software engineering , physics , thermodynamics
Maintainability indicates the extent to which maintenance can be done easily and quickly. The consideration of maintainability is crucial to reduce the operation and support costs of weapon systems, but if the maintainability is evaluated after the prototype production is done and necessitates design changes, it may increase the cost and delay the schedule. The evaluation should verify whether maintenance work can be performed, and support the designers in developing a design to improve maintainability. In previous studies, the maintainability index was calculated using the graph theory at the early design phase, but evaluation accuracy appeared to be limited. Analyzing the methods of evaluating the maintainability using fuzzy logic and 3D modeling indicate that the design of a system with good maintainability should be done in an integrated manner during the whole system life cycle. This paper proposes a method to evaluate maintainability using SysML-based modeling and simulation technique and fuzzy logic. The physical design structure with maintainability attributes was modeled using SysML 'bdd' diagram, and the maintainability was represented by an AHP matrix for maintainability attributes. We then calculated the maintainability using AHP-based weighting calculation and fuzzy logic through the use of SysML 'par' diagram that incorporated MATLAB. The proposed maintainability model can be managed efficiently and consistently, and the state of system design and maintainability can be analyzed quantitatively, thereby improving design by early identifying the items with low maintainability.
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