## 3 Signal Processing and Artificial Intelligence for DT Development
### Artificial Intelligence with DT
They are generally more complex and need more data to achieve optimal performance, which limits their use in scenarios with data scarcity. Refs. [101; 102] propose two ideal learning combinations to address the issue of data scarcity by using generative networks to create synthetic data. However, they suffer from convergence issues, and the synthetic data often fail to reflect the physical nature of the phenomenon, limiting their applicability. The authors of [103] point out that SVMs are relatively simple networks and are not capable of capturing complex relationships, especially for multi-class tasks. They can handle relatively simple binary classification tasks, as shown in [21], or be preceded by an optimization step to facilitate classification, as in [70]. Table 2 summarizes all the works presented in this section, which focus on deploying DTs for gearbox monitoring based on AI techniques, as well as the addressed limitations. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Methodology** & **Contributions** & **Strengths** & **Limitations** \\ \hline
[22] & Transfer learning on scalograms & Used pre-trained models for classification & Good for limited data & Hard to adapt to new domains \\ \hline
[94] & Meta-learning & Learns from small/complex datasets & Learns fast with few samples & Learning complexity \\ \hline
[95] & GNN & Adapts model to real data & Accurate fault detection & Needs strong graphical modeling \\ \hline
[96] & Transfer + Meta-learning & Combines transfer and meta-learning & Benefits from both methods & Inherits both drawbacks \\ \hline
[97] & GNN + regularization & Reduces sim-real data gap & Strong predictive power & High computational demand \\ \hline
[98] & GNN + BiLSTM & RUL estimation of bearings & Learns sequences + data adaptation & Needs graphical data + preprocessing \\ \hline
[99] & Branched network + CNN & Wear fault diagnosis & Accurate diagnosis & Needs a lot of training data \\ \hline
[21] & One-class SVM + CapsNet & Fault detection with auto-architecture & Outperforms state-of-the-art & CapsNet architecture may be complex \\ \hline
[51] & Transformer-based network & Transfers model data to test data & High learning capability & Requires large data and memory \\ \hline
[70] & Atom Search + SVM & Planetary gearbox fault diagnosis & Enhanced classification & Needs prior optimization \\ \hline
[100] & Transfer learning + attention modules & Improved tracking \& feature extraction & Better domain adaptation & Depends on source training domain \\ \hline
[101] & GAN + LSTM & Experimental data classification & Solves data scarcity & GAN convergence and physical realism issues \\ \hline
[102] & GAN + GRU & Fatigue estimation \& RUL prediction & Handles small data & Synthetic data lacks realism \\ \hline
[103] & Multi-class SVM & Simulation-trained fault classification & Simple \& interpretable & Weak on complex/multi-class problems \\ \hline
[49] & 1D CNN + FBC algorithm & Bearing fault diagnosis & Learns across domain gap & May overfit without regularization \\ \hline
[104] & Various domain adaptation algorithms & Applied to centrifugal pump algorithms & Versatile domain bridging & Domain adaptation can be unstable \\ \hline
[105] & Graph convolutional memory network & Degradation monitoring & Time-series modeling + sim-to-real projection & Graph representation required, complex training \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of DT approaches using AI.