Qualitative analysis of CNG dispensing system using fuzzy FMEA–GRA integrated approach

• Published in: International journal of system assurance engineering and management 2019-02, Vol.10 (1), p.44-56
• Main Authors: Panchal, Dilbagh, Srivastava, Priyank
• Format: Article
• Language: English
• Subjects: Air filters; Compressed gas; Decision analysis; Decision making; Dispensing; Engineering; Engineering Economics; Failure analysis; Failure modes; Fuzzy systems; Leakage; Logistics; Marketing; Natural gas; Organization; Original Article; Qualitative analysis; Quality Control; Reliability; Risk analysis; Safety and Risk; Support systems; Uncertainty analysis
• ISSN: 0975-6809; 0976-4348
• DOI: 10.1007/s13198-018-0750-9

This research work reviews different risk analysis approach and expounds the application of fuzzy integrated multi criteria decision making framework for qualitative analysis of compressed natural gas dispensing system. Qualitative analysis of system is its risk identification and prioritization. This analysis is done by exemplifying integrated approach of failure mode and effect analysis (FMEA), fuzzy FMEA and fuzzy grey relational analysis (GRA) respectively. The conventional FMEA prioritizes risk on the basis of risk priority number (RPN). The uncertainty issue from analysis has been removed by integrating fuzzy methodology with conventional technique. The rule base in fuzzy inference system is used for calculating fuzzy RPN. The effect of weightage of each variable has been considered in fuzzy GRA for risk prioritization. A total of 43 risks have been identified and seven are assessed critical to system. The regulator malfunction of metering skid, internal leakage of compressor, motherboard failure of dispenser, internal pipe leakage and air filter choking of priority panel and plug leakage of cascade have been identified as critical risk in present study. This outcome of the proposed framework will act as decision support system for the system analyst and maintenance engineer to identify and prioritize risk, subsequently assisting them for better maintenance planning.