**5. Conclusions**

Based on BRB, a new fault diagnosis model (FFBRB) based on fuzzy fault tree analysis theory is proposed. The FFBRB model expands the expert knowledge base of BRB based on the FFTA mechanism, uses the improved BRB as a fault diagnosis tool, and incorporates an optimization algorithm to further reduce the influence of uncertain factors in the model. The model has the following characteristics:

The FFBRB model has a stronger ability to acquire expert knowledge. The FFBRB model integrates an FFTA mechanism analysis into the BRB expert knowledge base, which makes the model more capable of describing problems.

The FFBRB model has stronger analytical and reasoning ability. By training and optimizing the sample data, the model further improves the accuracy of the data, and thus makes the model more accurate.

The FFBRB model has high accuracy. Compared with traditional data-driven methods the FFBRB processing results have higher accuracy.

The feasibility of the FFBRB model is verified by experiments, and its advantages are compared with the other two methods. Based on the FFBRB model proposed in this paper, the following two aspects can be further studied in the future: (a) the theoretical transformation of the FFTA and interval BRB; (b) other methods could be used to expand the expert knowledge base in the flywheel fault diagnosis; (c) the BRB is an interpretable modeling method, which provided an effective support for the construction of interpretable deep learning models. How to effectively construct a fault diagnosis model based on a deep BRB will be the main work in the next step.

**Author Contributions:** X.C. and S.L. contributed equally to this work. Conceptualization, X.C. and S.L.; methodology, X.C. and S.L.; software, Y.X.; validation, X.C., S.L. and W.H.; formal analysis, X.C. and S.L; investigation, J.S.; data curation, P.Z.; writing—original draft preparation, X.C.; writing— review and editing, X.C. and W.H.; visualization, X.C.; supervision, W.H. and B.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by the Postdoctoral Science Foundation of China under gran<sup>t</sup> no. 2020M683736, in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No. LH2021F038, in part by the innovation practice project of college students in Heilongjiang Province under gran<sup>t</sup> no. 202010231009, 202110231024, 202110231155, in part by the graduate quality training and improvement project of Harbin Normal University under gran<sup>t</sup> no. 1504120015, in part by the graduate academic innovation project of Harbin Normal University under gran<sup>t</sup> no. HSDSSCX2021-120, HSDSSCX2021-29.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing not applicable.

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