## 3 Simulation Experiments
### Experimental Results and Analysis  
To validate the effectiveness of the proposed TCN-BiLSTM model for time-series fault prediction tasks, comparative simulation experiments with multiple models were conducted. Under the same dataset and experimental conditions, the proposed model was compared with RNN, CNN, BiLSTM, and A-BiLSTM models [24]. The final experimental results of the test set are illustrated in Figures 7 and 8, showcasing the performance and classification outcomes of various models during multiple iterations of the training process. An in-depth analysis of the experimental data reveals that all models exhibited a rapid convergence trend during the training phase. Examination of the iterative loss curves indicates that the loss values of the models began to stabilize after approximately 20 iterations, suggesting that these models possess relatively high training efficiency for  
Figure 8: Histogram of experimental results. Figure 7: Line chart of experimental results. the given task. Among them, the proposed TCN-BiLSTM model demonstrated superior convergence characteristics, achieving significantly lower loss values within a shorter training duration. This reflects its enhanced capacity for model fitting and generalization. Furthermore, the TCN-BiLSTM model consistently maintained high performance across key evaluation metrics, including Precision, Recall, and F1 Score, underscoring its robustness. When evaluating the experimental results on the test set, the TCN-BiLSTM model delivered consistently superior classification performance compared to the baseline models. Specifically, it achieved a 20.79% reduction in loss compared to the CNN model, highlighting its remarkable ability to capture temporal dependencies and process sequential data. While CNN excels at extracting localized features, its limited capacity for modeling long-range temporal dependencies constrained its performance in this task. Compared to the BiLSTM model, the TCN-BiLSTM model achieved a 16.67% reduction in loss. Although BiLSTM is proficient at modeling temporal features, its inability to fully leverage long-term dependencies limited its effectiveness, a shortcoming addressed by the introduction of the TCN architecture. Similarly, when compared to the A-BiLSTM model, the TCN-BiLSTM model achieved a 17.09% reduction in loss, and compared to the RNN model, it yielded a 15.54% reduction in loss. The dilated convolution and batch normalization mechanisms in the TCN-BiLSTM model effectively mitigate the impact of noise in observational data during the predictive phase. In contrast, while the CNN model has certain advantages in extracting local features, its relatively small receptive field makes it more susceptible to being misled by local noise, thereby reducing the accuracy of its predictions. Similarly, although the BiLSTM model is capable of capturing long-term dependencies in time series, it lacks the ability to model multi-scale temporal features. To further validate the performance of the model, significance testing experiments were conducted. Under the same configuration parameters, each model was independently repeated 20 times. The final results of each experiment on the test set are shown in the Figure 9 below. Figure 9: Boxplot of experimental results. The paired _t_-test is a commonly used statistical method primarily designed to assess whether the mean difference between paired observations is statistically significant. In this study, paired _t_-tests were performed on the loss values of different models to evaluate whether the performance differences between the TCN-BiLSTM model and other comparative models are significant.