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Fuzzy Inference Based Failure Modes and Effects Analysis (FMEA) for the Acceptance and Commissioning of a Ring-Gantry LINAC

J Chang*, S Jang , P Teo , R Lalonde , D Heron , M Huq , UPMC Hillman Cancer Center and University of Pittsburgh School of Medicine, Pittsburgh, PA


(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₎


Fuzzy Logic, Acceptance Testing, Commissioning


IM/TH- Formal quality management tools: Failure modes and effects analysis

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