A novel method for failure mode and effects analysis using fuzzy evidential reasoning and fuzzy Petri nets

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, X. 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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Keywords

  • Failure mode and effects analysis (FMEA)
  • Fuzzy evidential reasoning
  • Fuzzy Petri net (FPN)
  • Ship fire-safety system