**6. Conclusions**

Although FMECA has been extensively used in many fields for risk analysis, there are still some flaws that limit its performance of application in actual case, especially in terms of the issues of the representation of expert's opinions on the evaluation of failure modes, the aggregation of experts' diversity evaluations, and the determination of risk priorities of failure modes. In this paper, a new risk assessment model is proposed by using an integrated approach, which integrates the strong expressive ability of *Z*-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts' diversity evaluations, and the strength of VIKOR method to flexibly model multicriteria decision-making problems. Based on the integrated approach, the proposed risk assessment model has the follow advantage features compared to the traditional FMECA and its variant:


FMECA team to reach a feasible ranking results based on maximizing the group utility for the "majority" and minimizing the individual regre<sup>t</sup> for the "opponent".

To validate the performance of application in real case of the proposed FMECA approach and verify its effectiveness, the proposed risk assessment model is applied to the risk analysis of the failure modes in offshore wind turbine pitch system. By analyzing the ranking results of the twenty-four potential failure modes, we see that the proposed FMECA approach can be well used in real case, especially in the situations that the evaluations of experts are vague and uncertain and the failure modes are interacted with each other. Through the comparison with other approaches, we see that the ranking results obtained by proposed approach are more rational and more consistent with the actual results.

As a recommendation for future research, it is suggested that the evaluations of different experts for failure modes should be aggregated in the form of *Z*-number without converting the *Z*-numbers into crisp value, and some efficient fusion approaches should be excavated and applied to aggregation process. Moreover, the complexity of the proposed approach needs to be optimized to make it more applicable in practice. Moreover, in future work, the proposed model will be applied for risk managemen<sup>t</sup> decision making in other fields of quality and reliability engineering to further verify its effectiveness.

**Author Contributions:** Conceptualization, Z.W. and R.W.; methodology, Z.W.; software, R.W.; validation, W.D. and Y.Z.; formal analysis, R.W.; investigation, Z.W.; resources, Y.Z.; data curation, W.D.; writing—original draft preparation, Z.W.; writing—review and editing, Z.W. and R.W.; visualization, W.D.; supervision, Y.Z.; project administration, W.D.; funding acquisition, Y.Z. and R.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by [the science and technology project of China Huaneng Group Co., Ltd., Beijing 100031, China] gran<sup>t</sup> number [HNKJ20-H72-02].

**Acknowledgments:** The authors gratefully acknowledge the valuable cooperation of the Clean energy branch of Huaneng (Zhejiang) Energy Development Co., Ltd., Hangzhou 310005, China in accomplishing this research project.

**Conflicts of Interest:** The authors declare no conflict of interest.
