# Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference
## 5 Conclusions  
Bearing health is important for the working life of rotating machinery. This research focused on the intelligent diagnosis of bearing faults. Through accurate and systematic data preprocessing procedures and neural networks, the intelligent diagnosis of bearing faults  
Figure 27: Comparison of accuracy rates for two models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Accuracy** & **Energy Model** & **Recurrence Rate Model** \\ \hline Group 1 & 96.667\% & 96.667\% \\ Group 2 & 96.111\% & 97.778\% \\ Group 3 & 95.833\% & 97.500\% \\ Group 4 & 97.619\% & 99.048\% \\ Average & 96.557\% & 97.748\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of two models.  
was successfully performed. These included a CNN grounded in data energy analysis and recurrence characteristics. The proposed data preprocessing procedures and intelligent algorithms have demonstrated their efficiency and accuracy in categorizing diverse fault types, exhibiting high precision in identification and robust generalization. The main contributions of the paper can be summarized as follows:  
1. Recurrence quantity spectrums were defined to obtain a comprehensive dataset with enhanced features.
2. A machine learning framework based on a convolutional neural network (CNN) was constructed. The activation functions in the activation layer were optimized for better fault diagnosis. The feature matrices were specifically defined to identify the subtlest defects of bearings accurately.
3. Comparisons of energies, recurrence quantification, and amplitude-frequency characteristics for bearing fault detection were conducted to assess the accuracy, computational efficiency, and robustness.
4. Through the delineation of training and testing sets, an accuracy over 98% was obtained, accompanied by improved generalization ability and robustness.
5. This research developed a more efficient fault detection procedure and algorithms, which can serve as a universal model for practical bearing fault diagnosis in engineering.  
The proposed method and bearing fault diagnosis procedures are mainly based on theoretical algorithms. Future research can be planned to focus on hardware design and implementation for more practical application of the methods.  
Methodology, T.W.; validation, D.L.; investigation, S.C.; writing--original draft preparation, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [the Science and Technology Plan Project of State Administration for Market Regulation of China] grant number [2023 MK199], [the National Nature Science Foundation of China] grant number [52305175], [the National Key Research and Development Program of China] grant number [2023 YFC3010400], [the Science and Technology Program of CSEI] grant number [2021 XKTD013], and [the Key R&D and Transformation Plan Project of Qinghai Province] grant number [2023-QY-215].  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare they have no conflicts of interest.