Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System

Abstract

The reliable operation of a process plant is critical to the safety, performance, and profitability of a business. Failure Mode and Effects Analysis (FMEA) is a process of reviewing systems, subsystems, and equipment that identify potential failure modes, their root causes, and consequences. FMEA is also a risk assessment tool that has been used successfully in a wide range of process industries as an integral part of reliability-centered maintenance, safety management, and continuous improvement. The method has indeed been criticized, especially in the area of system assessment, but engineers still predominantly use traditional, unmodified FMEA best practices. In this study, a new conceptual model is proposed to improve the traditional technique and make FMEA a more autonomous, data-driven, and accurate method. The conceptual model of improved FMEA uses ANFIS and FIS models in one automated process that aims to solve the defect handling process from failure detection to quantification of risk level and prioritization of dedicated mitigation action.