### Extracting Research Gaps from Related Works
#### 1.2.3 Related-Work Analysis  
The study in [9] uses a collection of real-world cases, some of which include the previously mentioned aeronautical bearing dataset [7]. To identify sensitive features, minimize noise, and prevent the loss of important information, the authors suggested a multi-scale slip-averaging method based on sensitive multi-scale symbol dynamic entropy. This is an important breakthrough in feature engineering. The data are randomly divided into training and test sets using a 50% ratio, and classification is performed using a support vector machine. Three-fold cross-validation is used for SVM evaluation and tuning. While the endurance test associated with prognosis was not addressed, the challenges raised here are closely connected to diagnosis. Recall, precision, specificity, and F1 score were the four measures employed in the evaluation. Thus, this work did not target the problem of data unavailability and drift but only focused on data complexity. In [10], the authors suggested a full graph dynamic autoencoder, which consists of modules for graph attention and full connection autoencoders. Damage severity was not explored in this situation; rather, it was solely considered in terms of multi-condition fault identification (i.e., diagnosis). The method was also used in many examples, including the study's primary dataset. Dealing with automated feature extraction, denoising, imbalanced classification, and data drift were all part of this. However, in this case, only a small number of samples (i.e., hundreds) were used, and the problem complexity was lowered by dividing it into just two operating modes (i.e., healthy and unhealthy), even though the total number of data was massive (i.e., \(46,592,000\) samples), and the presence of seven operating modes greatly increased the prediction complexity. Furthermore, the suggested model was evaluated using the same metrics previously used in [9]. In general, this approach does not address data unavailability and complexity at any point. In [11], a multilayer wavelet attention convolutional neural network was suggested to provide machine fault diagnostics while overcoming the distorted effects of noise. Utilizing a discrete wavelet attention layer, physics-based knowledge is added to the deep network as an extra source of information. While other data complexity challenges (e.g., outliers) are not completely taken into account, an adaptive learning technique is employed to combat data drift. The discrete wavelet attention layer is used to address data unavailability. Although the exact method by which the authors employed the moving window to reduce the number of samples is unknown, it seems that about 75% of the samples were used for training, and the other 25%, for testing. Multi-scale three-dimensional Holo-Hilbert spectral entropy, a coarse-grained entropy-based processing technique, was developed in [12], and almost identical research was performed in [9] to extract features from bearing failures while addressing complexity challenges overall. Bat-optimized support vector machine, which is similarly employed for classification (i.e., fault diagnosis only), was then used. The work appears to have decent findings; nevertheless, the author's usage of a limited number of samples (30 for training and 20 for testing) was deemed insufficient for such analysis. Despite the findings' accuracy, it is impossible to generalize the obtained conclusions for such very complicated investigation. Additionally, this study did not focus on data drift or availability problems. The authors of [13] treated the problem of data unavailability and its drawbacks in the generalization process. To find out more about learning behavior in various settings and to acquire general knowledge, they employed transfer learning across working conditions. To address the issues of feature extraction (i.e., automated data engineering) and fault diagnosis, deep transfer learning based on graph convolutional networks was presented.