Risk assessment model of mining equipment failure based on fuzzy logic

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.

[1]  Carlos León,et al.  Operational Risk Management Using a Fuzzy Logic Inference System , 2009 .

[2]  Nan Liu,et al.  Risk evaluation approaches in failure mode and effects analysis: A literature review , 2013, Expert Syst. Appl..

[3]  Dejan Ivezic,et al.  Fuzzy approach to dependability performance evaluation , 2008, Qual. Reliab. Eng. Int..

[4]  Sanjay Kumar Chaturvedi,et al.  Criticality Assessment Models for Failure Mode Effects and Criticality Analysis Using Fuzzy Logic , 2011, IEEE Transactions on Reliability.

[5]  Milos Tanasijevic,et al.  Effectiveness assessment of agricultural machinery based on fuzzy sets theory , 2012, Expert Syst. Appl..

[6]  Jian-Bo Yang,et al.  Safety analysis and synthesis using fuzzy sets and evidential reasoning , 1995 .

[7]  Rita Gamberini,et al.  A fuzzy multi-attribute model for risk evaluation in workplaces. , 2009 .

[8]  Sukhamay Kundu.  The min-max composition rule and its superiority over the usual max-min composition rule , 1998, Fuzzy Sets Syst..

[9]  Chee Peng Lim,et al.  Enhancing the Failure Mode and Effect Analysis methodology with fuzzy inference techniques , 2010, J. Intell. Fuzzy Syst..

[10]  Jin Wang,et al.  Modified failure mode and effects analysis using approximate reasoning , 2003, Reliab. Eng. Syst. Saf..

[11]  P. Zalewski.  Risk assessment of LNG carrier systems failure using fuzzy logic , 2011 .

[12]  Shun-Peng Zhu,et al.  Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty , 2011 .

[13]  Jin Wang,et al.  Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritization of Failures in FMEA , 2008, IEEE Transactions on Reliability.

[14]  Zaifang Zhang,et al.  Risk prioritization in failure mode and effects analysis under uncertainty , 2011, Expert Syst. Appl..

[15]  Celso Marcelo Franklin Lapa,et al.  Fuzzy inference to risk assessment on nuclear engineering systems , 2007, Appl. Soft Comput..

[16]  Marcello Braglia,et al.  Fuzzy criticality assessment model for failure modes and effects analysis , 2003 .

[17]  Loon Ching Tang,et al.  Fuzzy assessment of FMEA for engine systems , 2002, Reliab. Eng. Syst. Saf..

[18]  Stephen N. Luko,et al.  Risk Management Principles and Guidelines , 2013 .

[19]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[20]  J. B. Bowles,et al.  Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis , 1995 .