## 3 Steel Truss Experiment
### Signal Recognition and Analysis
#### 3.3.3 Analysis of Results and Discussion
Furthermore, according to the recognition results of precision, recall, and F1 score for each category in Table 6, the recognition effect of using the MFCC-WPES combined features is also significantly better than that of using these two types of features alone. To evaluate the performance of the RF model, we tried various machine learning models, including Bayes, KNN, SVM, BP neural network, and XGBoost. These models were trained using the MFCC-WPES combined features, and their recognition performance was compared and analyzed. The specific recognition results are shown in Figure 12 and Table 7. The results clearly indicate that the XGBoost, SVM, and BP models exhibit relatively high recognition accuracy, all reaching approximately 0.98. However, they still perform below the accuracy of the RF model. In comparison, the KNN and Bayes models have lower accuracy, especially Bayes with only 0.903, which may suggest that these two models are not the best choices for handling the current task. Furthermore, from the per-class recognition results presented in Table 7, we can also observe that, except for slightly lower precision and recall in a few categories, the RF model outperforms other classifiers in F1 score for all categories. The analysis results above indicate that the combined MFCC-WPES features adopted in this paper can more comprehensively represent sound signals, and their recognition performance is significantly better than traditional MFCC and WPES. Meanwhile, when compared with several other machine learning models, the optimized Random Forest (RF) model exhibits better performance in key performance indicators, such as recognition accuracy and F1 score. Ultimately, this method not only achieves accurate identification of bolt loosening damage signals but also demonstrates good discrimination ability for different types of environmental noise signals. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Indicator** & **Feature** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{3}{*}{Precision} & MFCC & 1.00 & 0.93 & 0.91 & 0.97 & 1.00 & 0.96 & 1.00 & 0.92 \\  & WPES & 1.00 & 0.94 & 0.98 & 0.98 & 0.99 & 0.97 & 1.00 & 0.96 \\  & MFCC-WPES & 0.99 & 0.98 & 0.99 & 0.97 & 1.00 & 0.98 & 1.00 & 0.98 \\  & MFCC & 1.00 & 0.96 & 0.97 & 0.91 & 1.00 & 0.93 & 1.00 & 0.92 \\  & WPES & 1.00 & 0.97 & 0.98 & 0.97 & 1.00 & 0.96 & 0.99 & 0.93 \\  & MFCC-WPES & 1.00 & 0.99 & 0.97 & 0.99 & 0.99 & 0.97 & 1.00 & 0.99 \\  & MFCC & 1.00 & 0.94 & 0.94 & 0.94 & 1.00 & 0.95 & 1.00 & 0.92 \\ F1 score & WPES & 1.00 & 0.96 & 0.98 & 0.98 & 1.00 & 0.96 & 1.00 & 0.95 \\  & MFCC-WPES & 1.00 & 0.99 & 0.98 & 0.98 & 1.00 & 0.97 & 1.00 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recognition results of different training features. Figure 11: Comparison chart of recognition accuracy of different training features. In practical applications, the significance of the method proposed in this paper is mainly reflected in the following aspects compared with existing methods. Firstly, the excitation method using a small vibration exciter significantly simplifies the operation process and improves applicability compared with the traditional method of knocking bolts individually. Secondly, compared with traditional contact vibration signal measurement, the process of sound signal acquisition is simpler and faster. In practical applications, only ordinary recording equipment or even a mobile phone is required, greatly reducing the implementation threshold. Finally, the random forest recognition model adopted in this paper not only has a high accuracy rate but also exhibits strong robustness to noise, enabling effective processing of high-dimensional data. Additionally, it boasts low training costs and simple deployment, making it highly suitable for application in mobile devices and embedded systems. However, it must be pointed out that this research also has certain limitations and challenges.