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(Wednesday, 7/17/2019) 8:30 AM - 9:30 AM
Room: Stars at Night Ballroom 2-3
Purpose: Risk assessment for the acceptance testing and commissioning (ATC) of a Halcyon linac was performed based on a conventional FMEA process in our previous study¹. However, the risk priority number (RPN) that was generated with equal weightings from the three factors, severity (S), occurrence (O), and detection (D), contains limitations. In this study, we re-evaluated the potential failure modes (FM) based on the improved RPN generated with a fuzzy logic inference system, and compared the final FM ranking to those obtained from the traditional FMEA.
Methods: Gaussian membership functions of 4 levels (low, medium, high, and very high) for each risk factor were generated considering their relative importance. A set of fuzzy if-then rules derived from clinical evaluators were used to relate S, O, D membership function to the fuzzy RPN membership function. A centroid defuzzification method was then chosen to obtain a fuzzy RPN value. Using the same O, S, and D values from our previous FMEA study as inputs, new fuzzy RPNs were obtained.
Results: The fuzzy RPN ranking were found to be different from those obtained from our previous study. The top ranking of “failure in dose reproducibility with factory-calibrated MU” for acceptance remained unchanged, however, third ranking of “error in setup due to multiple options provided for reference dosimetry by the manufacturer” changed to the top ranking for commission. Since a membership function of S was constructed with a broader range of “very high” to increase a weight, decision rules affected the FMs with high S value.
Conclusion: The proposed fuzzy inference RPN presents a more reasonable and effective method for assessing potential FMs by incorporating a relative importance among the O, S, and D parameters and factoring in the evaluation uncertainty for the risk assessment of the ATC. ₍¹Teo et al, Med Phys. 2019₎
Keywords
Fuzzy Logic, Acceptance Testing, Commissioning
Taxonomy
IM/TH- Formal quality management tools: Failure modes and effects analysis
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