## 4 Results
### Experiments and Analysis
#### 4.2.3 Complete Signal Testing  
The above experiments are tested on segmented signal fragments, while real engineering applications require the detection of continuous signals, which are also more complex. To verify the performance of the proposed method in engineering applications, we tested the above-trained model on continuous signals and output the probability values in real time. Figures 18 and 19 show the comparison between the original signal and the external intrusion detection probability generated by the softmax function. Among them, the sensor in Figure 19 is further away from the percussive tap point and is more affected by noise, making it a harder task.  
As can be seen from Figure 18, the output probability of each model roughly matches the anomaly in the original signal (the mutated part of the blue signal). Among them, the output probability peaks of the CNN-LSTM-AM model are the most obvious and can better capture the anomalies; the prediction values of the CNN, LSTM, GRU, and Transformer models fluctuate a lot and are weakly robust against noise; the 1D ResNet model produces a large number of probability peaks near the anomalies, but it is also susceptible to the influence of noise, which leads to the false alarms. The STFT+2D ResNet model has a noise with a certain suppression effect and relatively few false alarms, while the CLDNN model  
Figure 18: Real-time output probability and raw signal amplitude visualization of the external intrusion detection model, where the red dots represent the time corresponding to the percussive tap.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **F1 Score (\%)** & **Parameters** &
\begin{tabular}{c} **Inference Time** \\ **(ms/Sample)** \\ \end{tabular} \\ \hline LCANet & 90.41 & 85.92 & 87,105 & 0.1269 \\ TEResNet (our) & 97.12 (+7.4\%) & 96.15 (+11.9\%) & 48,009 (\(-\)44.9\%) & 0.0480 (\(-\)62.2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of TEResNet with the latest lightweight network LCANet in terms of accuracy, F1 score, number of parameters, and inference speed.  
has a detection performance similar to that of the 1D ResNet, but is also susceptible to noise. The TEResNet performs well, with its external intrusion detection probability accurately corresponding to real anomalies.  
In the more challenging scenario, i.e., Figure 19, the output probabilities of the CNN-LSTM-AM model capture some of the anomalies, but the overall magnitude is small; the CNN, LSTM, GRU, Transformer, and 1D ResNet models show a lot of fluctuations in the normal signal region, leading to many false positives; the STFT+2D ResNet model mitigates the false positive problem to some extent, but it is still not completely eliminated; and the CLDNN model also has more false positives. The proposed model is still able to accurately detect anomalies in this complex scenario, and the output probability curves overlap strongly with the real anomalies, while showing strong robustness to noise interference. Overall, the model significantly outperforms other models in the continuous signal external intrusion detection task with a higher accuracy and robustness.