**4. Conclusions**

In this paper, an EMD-based statistic distance was proposed for the fault diagnosis of machinery. The proposed method made good use of the merit of the EEMD. Furthermore, a fault could be automatically diagnosed, which needed much fewer samples with labels than the intelligent method. The similarity of one signal between the referenced samples collected from the machinery with various faults could be accuracy evaluated. As a result, the signal had the same type with the referenced sample when they were most similar. The effectiveness of the proposed method was demonstrated by two real cases, including a bearing dataset and composite dataset of bearings and gears. It could be found that satisfying results were obtained even when there were a few samples with labels. Consequently, the proposed method can be further used in engineering, which will be considered in our further work.

**Author Contributions:** Data curation, T.Z.; Resources, P.H.; Software, P.H.; Writing—original draft, T.W.; Writing—review & editing, L.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by the Science and Technology Key Project of Henan province under Grant No. 212102210520 and 202102210370, and the Science and Technology Key Project in the High-tech Fields of Henan province, gran<sup>t</sup> no. 152102210123.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.

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