Title
A new fuzzy methodology-based structured framework for RAM and risk analysis.
Abstract
The aim of this paper is to propose a new hybridized framework for analyzing the performance issues of a chemical process plant by utilizing uncertain, imprecise and vague information. In the proposed framework, Fuzzy Lambda–Tau (FLT) approach has been used for computing reliability, availability and maintainability (RAM) parameters of the considered system. Further, for enhancing the RAM characteristics of the system, improved Fuzzy Failure Mode Effect Analysis (FMEA) approach is adopted. Under improved Fuzzy FMEA approach, defined Fuzzy linguistic rating values in the form of triangular and trapezoidal Fuzzy numbers have been assigned by the experts to each risk factor of the listed failure causes. The proposed framework is demonstrated with an industrial application in a chlorine production plant of a chemical process industry. The results show decreasing trend for system availability and deposition of solid Nacl, mechanical failure, corrosion due to wet chlorine, scanty lubrication, improper adsorption and valve malfunctioning are identified as the most critical failure causes for the considered system. A comparative performance analysis between the proposed framework, Fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS), Fuzzy evaluation based on distance from average solution (Fuzzy EDAS) and Fuzzy Vlse Kriterijumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR) are then carried out to show the competence of the proposed framework. It is expected that the analytical results would be highly useful in formulating an optimal maintenance policy for such complex systems and may also be used for improving performance of similar plants.
Year
DOI
Venue
2019
10.1016/j.asoc.2018.10.033
Applied Soft Computing
Keywords
Field
DocType
Chlorine production plant,Fuzzy Lambda–Tau,Failure causes,Improved Fuzzy FMEA,Performance comparison
EDAS,Failure mode and effects analysis,Mathematical optimization,Risk analysis (business),Fuzzy logic,Ideal solution,Optimal maintenance,Fuzzy number,Maintainability,Mathematics
Journal
Volume
ISSN
Citations 
74
1568-4946
1
PageRank 
References 
Authors
0.35
20
5