Risk assessment model of mining equipment failure based on fuzzy logic |
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| Affiliation: | 1. University of Belgrade, Technical Faculty in Bor, VJ 12, Bor, Serbia;2. University of Belgrade, Faculty of Mining and Geology, Djusina 7, Belgrade, Serbia;1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China;2. Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing 100190, PR China;1. Department of Mathematics, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India;2. Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad 500 032, India;1. Department of Management Sciences, University of Waterloo, Waterloo, ON, Canada;2. HEC Paris, Strategy and Business Policy, I rue de la Liberation, Dept SPE, W1-214, 78351, Jouy-en-Josas, France;1. Faculty of Engineering, University of Kragujevac, Serbia;2. Clinic of Urology and Nephrology, Kragujevac, Serbia;3. Military Medical Academy, Belgrade, Serbia;1. Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China |
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| Abstract: | The systematic maintenance of mining machinery and equipment is the crucial factor for the proper functioning of a mine without production process interruption. For high-quality maintenance of the technical systems in mining, it is necessary to conduct a thorough analysis of machinery and accompanying elements in order to determine the critical elements in the system which are prone to failures. The risk assessment of the failures of system parts leads to obtaining precise indicators of failures which are also excellent guidelines for maintenance services. This paper presents a model of the risk assessment of technical systems failure based on the fuzzy sets theory, fuzzy logic and min–max composition. The risk indicators, severity, occurrence and detectability are analyzed. The risk indicators are given as linguistic variables. The model presented was applied for assessing the risk level of belt conveyor elements failure which works in severe conditions in a coal mine. Moreover, this paper shows the advantages of this model when compared to a standard procedure of RPN calculating – in the FMEA method of risk assessment. |
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| Keywords: | Risk assessment Fuzzy logic Min–max composition Mining equipment |
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