## 5 Conclusions  
Intelligent fault diagnosis plays a critical role in working condition maintenance for rotating machinery, significantly enhancing the operational efficiency and reliability of industrial equipment. To improve the diagnostic performance of intelligent fault diagnosis methods for various types of faults in rotating machinery, this article proposes a novel algorithm that combines EMD, IMFE, LDA, and SVM. The proposed IMFE offers advantages of few adjustable parameters, strong algorithmic robustness, and effective discrimination across different signal types. Experimental results were shown to assess the robustness of IMFE across varying time lengths and its recognition performance for different signals, verifying the stability of IMFE in feature extraction. The proposed method was compared with several other diagnostic methods for rotating machinery. The diagnostic results demonstrate a competitive edge over traditional diagnostic models, validating the strong diagnostic capabilities of this algorithm.  
Figure 10: Detailed diagnosis results of different methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Average Accuracy** & **Max Accuracy** & **Min Accuracy** \\ \hline
**Method 1** (EMD(3) \& FE \& SVM) & 88.88\% & 91.42\% & 85.41\% \\
**Method 2** (EMD(7) \& FE \& SVM) & 94.54\% & 96.14\% & 92.70\% \\
**Method 3** (EMD(7) \& FE \& + WF(FE) \& SVM) & 94.83\% & 96.12\% & 93.10\% \\
**Method 4** (EMD(7) \& FE \& WF(FE) \& SVM) & 94.71\% & 96.12\% & 93.10\% \\
**Method 5** (EMD(3) \& MFE \& SVM) & 85.40\% & 87.55\% & 83.69\% \\
**Method 6** (EMD(3) \& IMFE \& LDA \& SVM, proposed method) & 98.41\% & 99.14\% & 97.42\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical diagnosis results of the six methods in CASE 2.  
This article proposes an efficient and convenient fault diagnosis method for rotating machinery, which enhances equipment safety and stability while reducing maintenance costs. However, as the experiments were based solely on single-sensor signals, challenges remain concerning information loss and uncertainty. Future work will focus on improving the model to enable diagnostic applications with multi-source signals, further enhancing diagnostic accuracy and reliability.  
Conceptualization, J.W. and Y.P.; methodology, J.W. and Y.P.; software, Y.P.; validation, X.F., K.W. and Y.C.; formal analysis, J.W. and Y.C.; investigation, Y.C.; data curation, T.F.; writing--original draft preparation, Y.P.; writing--review and editing, J.W. and X.F.; visualization, J.W. and Y.P.; supervision, J.W.; project administration, T.F.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Natural Science Foundation of Higher Education Institutions of Anhui Province under grant 2024 AH050154; the Open Project of Anhui Province Key Laboratory of Special and Heavy Load Robot under grant TZJQR005-2024; the Project of the Anhui International Joint Research Center for Metallurgical Process and System Science, Anhui University of Technology, under grant 2023002; and the Open Project of the Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, under grant CS2023-ZD01.  
The data will be made available by the authors upon request.  
The authors declare no conflicts of interest.