## 5 Discussion  
Traditional FMEA used exact numbers to evaluate the risk factors and employed the RPN, which was computed through Equation (1), to prioritize failure modes. By contrast, the proposed approach used fuzzy linguistic terms to evaluate the risk factors, and the results were then aggregated through SAM. On the basis of the aggregated values, a fuzzy logic system was built to derive the FRPN that was used to prioritize failure modes. A comprehensive experiment was carried out where the values for occurrence, severity, and detection, along with the corresponding RPNs, were systematically obtained by the team members, following the guidelines stipulated in traditional FMEA. To compare the results, we placed the FRPNs from our approach alongside the RPNs from traditional FMEA, as shown in Table 15. The results show that our approach is more effective in generating unique values. It differentiated between failure modes that were indistinguishable using traditional FMEA. For instance, failure modes No. 1 and No. 16 had identical RPNs (224) with traditional FMEA. However,  
Figure 8: Primary user interface of the revised IVIS. with our new approach, they had different FRPNs (6.395 and 6.384). In addition, if we only used severity to prioritize failure modes, many failure modes would be indistinguishable. For instance, failure modes No. 10, No. 11, No. 14, No. 15, No. 19, and No. 21 all had an identical severity level of 6. Obviously, the FRPNs from our approach provided a clear ranking of the failure modes, each with different priorities. This clear ranking can offer valuable insights that improve the UX of interactive system design. The comparison between the proposed approach and two other typical approaches is presented in Table 16. In the approach that combined FMEA with fuzzy TOPSIS [64], the weights of the risk factors--occurrence, severity, and detection--were taken into account when calculating the FRPN. This results in a greater variety of unique FRPN values, thereby facilitating the prioritization of failure modes. However, in this combined approach, the FRPN was derived through fuzzy algebra operations, and the relationship between the risk factors and the FRPN was not fully addressed; therefore, there may be some bias in the results of this combined approach. For example, in the case of failure mode No. 3, the prioritizations obtained from the other two approaches are both ranked 1, while the prioritization from the approach that combined FMEA with fuzzy TOPSIS is ranked 2. In the approach that combined FMEA with fuzzy logic [13], the input values for the fuzzy logic system were derived from the defuzzification values, which were calculated based on the average of the fuzzy evaluation results. Consequently, identical FRPNs may occur, making it challenging to determine the prioritization of failure modes. For example, the prioritizations for failure modes No. 4, No. 5, No. 7, and No. 12 are identical. Clearly, the proposed approach, which integrates FMEA with SAM and fuzzy logic, overcomes the limitations of the two other typical approaches. Based on these findings, we believe our new approach offers several advantages, including more accurate results, ease of understanding, and flexibility in practical applications. In order to compare the UX of the original IVIS with the newly revised version, an A/B test was conducted, and the system usability scale (SUS) was employed. SUS was developed by [PERSON] [65] and has been extensively used in measuring the UX of interactive systems. It comprises 10 statements to which users are asked to express their level of agreement. The statements are equally divided between positive and negative wording. In this scale, a score of 0 represents the worst possible outcome, while 100 represents the best. We enlisted 30 participants for this UX test, ensuring a balance in gender and an age range between 30 and 60. Each participant had at least 5 years of IVIS experience, ensuring a solid understanding and familiarity with such systems.