Abstract

Failure Modes and Effects Analysis (FMEA) is an engineering technique widely used to identify, evaluate, and eliminate known or potential failures, problems, and errors and inform risk management decisions. Traditional FMEA uses the risk priority number (RPN) to identify potential failure modes and their levels. However, it displays shortcomings in determining risk factor weights, ranking risk priorities of failure modes, and dealing with uncertainties during the risk assessment process. To address these issues, this paper proposed an improved FMEA risk assessment method by integrating FMEA, the analytic hierarchy process (AHP), the technique for order preference by similarity to an ideal solution (TOPSIS), and the cloud model (CM) to determine a more reasonable failure mode level. First, the CM was used to improve the FMEA framework structure to comprehensively assess ambiguity and randomness during the evaluation process. Second, the cloud weights of the risk factor severity (S), occurrence (O), and detectability (D) were calculated using AHP and CM to determine the relative risk factor importance. Then, the cloud distance measurement algorithm and TOPSIS were combined into the TOPSIS-CM risk ranking method to objectively identify the failure modes requiring improvement. Finally, a case study of a flange connection system at a hydrogen blending transmission site in northern China was conducted to verify the adaptability of the proposed method in failure mode assessment. Compared with traditional FMEA methods, the decision scores obtained using the improved FMEA were more discriminating and distinguished the risk of each failure mode, providing more accurate information to decision makers (DMs).

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call