Abstract
Failure mode and effects analysis (FMEA) has been broadly used in various industries to ensure the safety and reliability of high-risk systems. As a meritorious risk management tool, it can identify, evaluate and eliminate potential failure modes in a system for remedial actions. Nevertheless, the traditional FMEA has suffered from many deficiencies, especially in the assessment of failure modes, the weighting of risk factors, and the calculation of RPN. Therefore, this paper presents a novel FMEA method based on fuzzy evidential reasoning and fuzzy Petri nets (FPNs) to improve the classical FMEA. In this model, belief structures are used to capture the uncertainty and fuzziness of the subjective assessments given by experts and a rule-based FPN model is established to determine the risk priority of the failure modes identified in FMEA. An empirical case concerning the risk evaluation of a ship fire-safety system is provided to illustrate the practicality and effectiveness of the proposed FMEA. The results show that the new risk assessment method can produce more reliable risk ranking results of failure modes.
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Acknowledgements
The authors are very grateful to the respected editor and the anonymous referees for their insightful and constructive comments, which helped to improve the overall quality of the paper. This work was partially supported by the National Natural Science Foundation of China (Nos. 61773250, 71671125 and 71432007) and the Program for Shanghai Youth Top-Notch Talent.
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Appendix: Fuzzy rules based on expert knowledge
Appendix: Fuzzy rules based on expert knowledge
No. | O | S | D | Risk | No. | O | S | D | Risk |
|---|---|---|---|---|---|---|---|---|---|
1 | Remote | Almost none | Certain | Very low | 64 | Moderate | Medium | Low | High |
2 | Remote | Almost none | High | Very low | 65 | Moderate | Medium | Very low | High |
3 | Remote | Almost none | Medium | Very low | 66 | Moderate | High | Certain | Low |
4 | Remote | Almost none | Low | Low | 67 | Moderate | High | High | Low |
5 | Remote | Almost none | Very low | Low | 68 | Moderate | High | Medium | Medium |
6 | Remote | Low | Certain | Very low | 69 | Moderate | High | Low | Medium |
7 | Remote | Low | High | Low | 70 | Moderate | High | Very low | High |
8 | Remote | Low | Medium | Low | 71 | Moderate | Very high | Certain | High |
9 | Remote | Low | Low | Low | 72 | Moderate | Very high | High | Medium |
10 | Remote | Low | Very low | Medium | 73 | Moderate | Very high | Medium | Medium |
11 | Remote | Medium | Certain | Very low | 74 | Moderate | Very high | Low | High |
12 | Remote | Medium | High | Low | 75 | Moderate | Very high | Very low | High |
13 | Remote | Medium | Medium | Low | 76 | High | Almost none | Certain | Very low |
14 | Remote | Medium | Low | Low | 77 | High | Almost none | High | Very low |
15 | Remote | Medium | Very low | Low | 78 | High | Almost none | Medium | Low |
16 | Remote | High | Certain | Low | 79 | High | Almost none | Low | Low |
17 | Remote | High | High | Low | 80 | High | Almost none | Very low | Low |
18 | Remote | High | Medium | Medium | 81 | High | Low | Certain | Very low |
19 | Remote | High | Low | Medium | 82 | High | Low | High | Low |
20 | Remote | High | Very low | Medium | 83 | High | Low | Medium | Low |
21 | Remote | Very high | Certain | Low | 84 | High | Low | Low | Medium |
22 | Remote | Very high | High | Medium | 85 | High | Low | Very low | Medium |
23 | Remote | Very high | Medium | Medium | 86 | High | Medium | Certain | Low |
24 | Remote | Very high | Low | Medium | 87 | High | Medium | High | Low |
25 | Remote | Very high | Very low | High | 88 | High | Medium | Medium | Medium |
26 | Low | Almost none | Certain | Very low | 89 | High | Medium | Low | Medium |
27 | Low | Almost none | High | Very low | 90 | High | Medium | Very low | Medium |
28 | Low | Almost none | Medium | Very low | 91 | High | High | Certain | Medium |
29 | Low | Almost none | Low | Low | 92 | High | High | High | Medium |
30 | Low | Almost none | Very low | Low | 93 | High | High | Medium | Medium |
31 | Low | Low | Certain | Very Low | 94 | High | High | Low | High |
32 | Low | Low | High | Low | 95 | High | High | Very low | High |
33 | Low | Low | Medium | Low | 96 | High | Very high | Certain | Medium |
34 | Low | Low | Low | Medium | 97 | High | Very high | High | Medium |
35 | Low | Low | Very low | Medium | 98 | High | Very high | Medium | High |
36 | Low | Medium | Certain | Low | 99 | High | Very high | Low | High |
37 | Low | Medium | High | Medium | 100 | High | Very high | Very low | Very high |
38 | Low | Medium | Medium | Medium | 101 | Very high | Almost none | Certain | Very low |
39 | Low | Medium | Low | Medium | 102 | Very high | Almost none | High | Very low |
40 | Low | Medium | Very low | High | 103 | Very high | Almost none | Medium | Low |
41 | Low | High | Certain | Medium | 104 | Very high | Almost none | Low | Low |
42 | Low | High | High | Medium | 105 | Very high | Almost none | Very low | Low |
43 | Low | High | Medium | Medium | 106 | Very high | Low | Certain | Low |
44 | Low | High | Low | High | 107 | Very high | Low | High | Low |
45 | Low | High | Very low | High | 108 | Very high | Low | Medium | Low |
46 | Low | Very high | Certain | Medium | 109 | Very high | Low | Low | Medium |
47 | Low | Very high | High | Medium | 110 | Very high | Low | Very low | Medium |
48 | Low | Very high | Medium | Medium | 111 | Very high | Medium | Certain | Low |
49 | Low | Very high | Low | High | 112 | Very high | Medium | High | Low |
50 | Low | Very high | Very low | High | 113 | Very high | Medium | Medium | Medium |
51 | Moderate | Almost none | Certain | Very low | 114 | Very high | Medium | Low | Medium |
52 | Moderate | Almost none | High | Very low | 115 | Very high | Medium | Very low | Medium |
53 | Moderate | Almost none | Medium | Low | 116 | Very high | High | Certain | Medium |
54 | Moderate | Almost none | Low | Low | 117 | Very high | High | High | Medium |
55 | Moderate | Almost none | Very low | Medium | 118 | Very high | High | Medium | Medium |
56 | Moderate | Low | Certain | Low | 119 | Very high | High | Low | High |
57 | Moderate | Low | High | Low | 120 | Very high | High | Very low | High |
58 | Moderate | Low | Medium | Medium | 121 | Very high | Very high | Certain | Medium |
59 | Moderate | Low | Low | Medium | 122 | Very high | Very high | High | High |
60 | Moderate | Low | Very low | Medium | 123 | Very high | Very high | Medium | High |
61 | Moderate | Medium | Certain | Medium | 124 | Very high | Very high | Low | Very high |
62 | Moderate | Medium | High | Medium | 125 | Very high | Very high | Very low | Very high |
63 | Moderate | Medium | Medium | Medium |
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Shi, H., Wang, L., Li, XY. et al. A novel method for failure mode and effects analysis using fuzzy evidential reasoning and fuzzy Petri nets. J Ambient Intell Human Comput 11, 2381–2395 (2020). https://doi.org/10.1007/s12652-019-01262-w
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DOI: https://doi.org/10.1007/s12652-019-01262-w