## 3 Signal Processing and Artificial Intelligence for DT Development
### Artificial Intelligence with DT  
The purpose of artificial intelligence (AI) is to emulate human reasoning, particularly in diagnosing faults by identifying fault types and estimating the health state for prognosis [91]. AI, in its basic form, is a set of rules designed to mimic human thought processes. Over time, it evolved into machine learning (ML), which involves training a network on data and testing it on unseen data, allowing the network to learn from the training data. However, machine learning has limitations when dealing with large datasets or performing complex tasks. To address these limitations, researchers developed more complex networks with a substantial number of parameters to optimize configurations and achieve better performance [92]. Learning in AI is broadly divided into two main categories: supervised learning and unsupervised learning. Supervised learning relies on labeled data, whereas unsupervised learning operates with unlabeled data. Within these two categories, two primary tasks are classification and regression. For monitoring purposes, classification is typically employed for detection and diagnosis, whereas regression is generally utilized for prognosis [93]. DT has always been combined with AI to enable autonomous and real-time monitoring. [PERSON] et al. [22] used transfer learning as a classifier for scalograms. [PERSON] et al. [94] based their approach on meta-learning, which allows the developed network to learn from small or complex datasets. [PERSON] et al. [95] applied graph neural networks (GNNs) to adapt model data to real-world data, achieving accurate fault detection. [PERSON] et al. [96] combined transfer learning, as in [22], with meta-learning from [94], leveraging the advantages of pre-trained networks for better classification while benefiting from meta-learning's ability to learn from limited data. Similarly, [PERSON] et al. [97] employed non-Euclidean convolution graph neural networks combined with standard regularization to minimize discrepancies between simulated and real-world data, improving predictive capabilities. [PERSON] et al. [98] also used GNNs, as in [95; 97], for data adaptation but integrated BiLSTM to analyze temporal \begin{table}
\begin{tabular}{p{56.9 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
[85] & DWT & Time–frequency feature extraction, signal alignment & Good adaptability & High computational cost, sensitive wavelet choice \\ \hline
[86] & Lifting wavelets & Feature extraction and data distribution alignment & Powerful for differing distributions & Complex implementation, requires prior wave study \\ \hline
[87] & Frequency decomposition & Align physical and twin system signals & Effective signal matching & Limited in non-stationary regimes \\ \hline
[23] & VMD, meshing frequency correlation & Separate noise and fault signatures & Realistic degraded signal synthesis & VMD parameter tuning required, often needs optimization \\ \hline
[88] & Spectral analysis & Align model and experimental data & Experimental validation & Less effective on complex signals \\ \hline
[89] & Markov transfer, time–frequency feature extraction & Adapt signals via image transformation & Captures temporal and spectral features & Complex to implement, signal-dependent \\ \hline
[90] & RMS, fuzzy Gauss–Laguerre autoregression & Bearing monitoring, crack severity classification & Effective statistical approach & Sensitive to operating mode changes \\ \hline
[69] & Hash algorithm, Hamming distance & Similarity measure between real and simulated fault classes & Robust under variable conditions & Dependent on data encoding method \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._sequences, utilizing its memory capabilities to estimate the RUL of bearings. [PERSON] et al. [99] combined a deep branched network with a CNN for accurate diagnosis of wear faults in bearings. [PERSON] et al.