## 3 Simulation Experiments
### Model Scalability  
The TCN-BiLSTM model demonstrates scalability and is capable of adapting to a wide range of real-world application scenarios. For instance, in high-dimensional small-sample problems where the data sample size is limited but the feature dimensions are high, traditional algorithms often struggle to handle such complex situations. The TCN-BiLSTM model, through the following mechanisms, exhibits its potential and scalability when applied to high-dimensional small-sample datasets.  
When dealing with high-dimensional datasets, the dilated convolution operation in the TCN-BiLSTM model expands the receptive field, enabling the extraction of multi-level temporal dynamic features over a longer time span. This characteristic allows the model to effectively capture useful signals within the data while reducing the interference of redundant features. The ability to extend the receptive field can be generalized to other high-dimensional small-sample problems. For example, in application scenarios involving large volumes of sensor data, while the number of features might be extremely high, the effective sample size could be quite limited. The scalability of the TCN-BiLSTM model facilitates learning significant features from limited data, thereby enhancing its effectiveness in other high-dimensional small-sample problems.  
Through its bidirectional learning mechanism, the TCN-BiLSTM model is able to capture dependencies between previous and subsequent time steps simultaneously. This mechanism contributes to its scalability in small-sample datasets. Small-sample problems are prevalent across various industrial domains, particularly in equipment fault prediction, where certain fault patterns may only manifest during a few time points. By enhancing the model's contextual understanding of time-series data, BiLSTM enables the extraction of latent long-term dependencies even when the sample size is limited.  
To further enhance the model's adaptability to broader applications, a series of optimization techniques have been incorporated during the network design and training process. These techniques not only improve performance on current tasks but also provide support for future expansions. Batch normalization assists the model in achieving faster and more stable training across diverse tasks, accommodating variations in data distributions. The Leaky ReLU activation function addresses the \"dead neuron\" problem that may arise when processing high-dimensional data, ensuring model stability in broader application scenarios. The dropout mechanism prevents overfitting while simultaneously enhancing the model's predictive capabilities on unseen data. With the integration of these techniques, the TCN-BiLSTM model exhibits enhanced generalizability, making it expandable to different domains and datasets, thereby boosting its potential for cross-domain applications.  
The TCN-BiLSTM model showcases outstanding scalability, making it applicable to a broader range of industrial fields and complex data types. Through continued algorithmic optimization and technological innovation, the application scenarios and processing ca pabilities of the model are expected to expand further, driving its adoption across more diverse fields.