FMEA Based on Interval 2-Tuple Linguistic GRA Method and Its Application to C-Arm X-Ray Machine
FMEA is a group decision behavior and generally performed by a cross-functional team. Multiple experts tend to express their judgments on failure modes by using multi-granularity linguistic term sets, and there usually exist uncertain and incomplete asses
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FMEA Based on Interval 2-Tuple Linguistic GRA Method and Its Application to C-Arm X-Ray Machine
FMEA is a group decision behavior and generally performed by a cross-functional team. Multiple experts tend to express their judgments on failure modes by using multi-granularity linguistic term sets, and there usually exist uncertain and incomplete assessment information. In this chapter, we present a novel FMEA method combining interval 2-tuple linguistic variables with gray relational analysis (GRA) to capture FMEA team members’ diversity opinions and improve the effectiveness of the traditional FMEA. At last, an empirical example of C-arm X-ray machine is given to illustrate the potential applications and benefits of the proposed method.
12.1 Introduction FMEA, first proposed by the aerospace industry in the 1960s, has been extensively used as a powerful tool for safety and reliability analysis of products and processes in a number of industries (Akyuz and Celik 2018; Baghery et al. 2018; Faiella et al. 2018; Mangla et al. 2018; Sayyadi Tooranloo et al. 2018). Different from other risk assessment tools, the major concern of FMEA is to emphasize the prevention of problems, rather than finding a solution after the failure happens. This can help team members adjust the existing programs, increase compensating provisions, employ the recommended actions to reduce the likelihood of failures, decrease the probability of failure rates, and avoid hazardous accidents. In order to analyze a specific product or system, a cross-functional team should be established first for carrying out FMEA. With respect to the scores of RPNs, all possible failure modes are ranked and proper actions will be preferentially taken on the high-risk failure modes. The conventional RPN method has been proved to be a useful tool for assigning limited resources to the most serious risk items; however, it has been criticized for a number of drawbacks (Carpitella et al. 2018; Hu et al. 2018; Kim and Zuo 2018; Liu et al. 2018, 2019; Yazdi 2018; Lo et al. 2019):
© Springer Nature Singapore Pte Ltd. 2019 H.-C. Liu, Improved FMEA Methods for Proactive Healthcare Risk Analysis, https://doi.org/10.1007/978-981-13-6366-5_12
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• The relative importance among O, S, and D is not taken into consideration. This may not be the case when considering a practical application of FMEA. • Different combinations of O, S, and D may produce exactly the same value of RPN, but their hidden risk implications may be totally different. • The three risk factors are difficult to be precisely evaluated. Much information in FMEA is often uncertain or vague and can be expressed in a linguistic way. • The conversion of scores is dissimilar for the three risk factors. The relationship between O and the associated ratings is nonlinear, while it is linear for that between D and the associated ratings. • The mathematical formula for calculating RPN is questionable and debatable. There is no rationale in obtaining the RP
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