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Integrated method utilizing graph theory and fuzzy logic for safety and reliability assessment of airborne systems
Author(s) -
Luboš Janhuba,
Jiří Hlinka,
Rostislav Koštial
Publication year - 2019
Language(s) - English
Resource type - Conference proceedings
DOI - 10.13164/conf.read.2018.4
Subject(s) - fuzzy logic , computer science , reliability (semiconductor) , reliability engineering , graph theory , reliability theory , artificial intelligence , engineering , mathematics , failure rate , power (physics) , physics , quantum mechanics , combinatorics
This paper presents integrated algorithm for airborne system safety and reliability assessment. In general aviation (mostly up to EASA CS-23) and non-military unmanned aerial vehicles industry, safety and reliability assessment process still relays almost exclusively on human judgment. Recommended practices define processes for system modelling and safety assessing are based on analyst understanding of a particular system. That is difficult and time-consuming process. Commercial computation aids are extremely expensive with restricted (or closed) access to the solution algorithms. Together with this problem, rapid development of modern airborne systems, their increasing complexity, elevates level of interconnection. Therefore, safety and reliability analyses have to continuously evolve and adapt to the extending complexity. Growing expansion brings in the field of unnamed aerial vehicles systems which consist of items without relevant reliability testing. Presented algorithm utilizes graph theory and fuzzy logic in order to develop integrated computerized mean for reliability analysis of sophisticated, highly interconnected airborne systems. Through the usage of graph theory, it is possible to create model of particular systems and its sub-systems in the form of universal data structure. Algorithm is conceived as fuzzy expert system, that emulates decision making of a human expert. That brings opportunity to partially quantify system attributes and criticality. Criticality evaluation increases level of assessment correlation with real state of system and its attributes.

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