## 3 Signal Processing and Artificial Intelligence for DT Development
### Artificial Intelligence with DT
[21] utilized a combination of a one-class support vector machine for fault state detection and a CNN variant called CapsNet, which automatically designs its architecture. This combination outperformed the state of the art for bearing fault diagnosis. [PERSON] et al. [51] relied on a transformer-based network to learn from bearing model data and adapt it to experimental test data. [PERSON] et al. [70] combined atom search optimization with a support vector machine, training it on simulated data and testing it on experimental data for planetary gearbox diagnosis. [PERSON] et al. [100] enhanced fault classification through transfer learning, integrating additional modules called the Similarity Attention Module and an Efficient Channel Attention module. This approach improved feature extraction and tracking between simulated and real-world data. [PERSON] et al. [101] utilized a transfer model reinforced with a generative adversarial network (GAN) for deep feature extraction and a long short-term memory (LSTM) network to extract features from time series. This combination enabled the classification of unseen experimental data generated by the DT. [PERSON] et al. [102] employed a GAN to generate data from simulated bearing model data. Regression for fatigue estimation and RUL prediction was performed using an encoder-decoder-based gated recurrent unit (GRU). [PERSON] et al. [103] focused on fault classification using a multi-class support vector machine trained on simulation signals and tested on experimental data. [PERSON] et al. [49] used a one-dimensional convolutional neural network with a first-layer wide convolutional kernel for bearing fault diagnosis. The first layer, with a large \(16\times 1\) kernel, enables global learning of information between simulated and real signals, considering the inevitable differences between them. This network is enhanced by the Feature Boosting and Compression algorithm, which helps mitigate forgetting and improve classification efficiency. [PERSON] et al. [104] implemented various domain adaptation methods to bridge the gap between simulated and real data, including Domain Adaptation Network, Maximum Classifier Discrepancy, and Self-Ensembling for Visual Domain Adaptation. These algorithms were applied to detect bearing faults in a centrifugal pump. [PERSON] et al. [105] studied degradation monitoring using a dynamic bearing model and real data, relying on a graph convolutional memory network to learn degradation simulation and project it onto real monitoring data. The learning network used is designed for time series and was compared with similar networks to demonstrate its effectiveness. Refs. [95; 98] emphasize that this type of network requires a good graphical representation to adapt simulation signals to real signals. Additionally, studies have shown the necessity of providing a large quantity of data, which must also be preprocessed. Ref. [96] is expected to deliver good results compared to the framework presented in [94], which is based on meta-models, and [22; 100], which rely on transfer learning networks. However, it also inherits their drawbacks. On one hand, there is the complexity of learning with meta-models, and on the other, the challenge of adapting new data to the data on which the transfer model was initially trained, which has been thoroughly detailed in [106]. It is worth noting that the attention mechanism introduced in [100] enhances its feature extraction efficiency and simplifies the task of the transfer network. Ref. [97] proposes a combination that addresses the complex nature of monitoring signals but, at the same time, limits the possibility of real-time deployment and increases the computational resources required. Ref. [99] presents a robust combination of branched networks and CNNs, which is effective but demands a significant amount of data for training. Ref. [51] highlights that transformer networks require significantly more computational resources and memory due to their reliance on attention mechanisms.