## 3 Methods, Application, and Result Discussion
### Proposed Approach
This is highlighted by the more rounds needed for further complex conditions, as shown in Table 3;
3. The training process showcases the effectiveness of LSTM by retaining adaptive learning, especially for less complex datasets. This is why it needs to be boosted by transfer learning and collaborative aggregation;
4. The best results on the endurance set were obtained quickly compared with the variable-speed and -load experiment. This was due to the benefits of directly transferring the learning parameters from the fifth round (i.e., holding enough additional information across conditions) to the first learning round. 5. The improvement rates of performance in both diagnosis and prognosis also reflect the importance of the procedure in the training process. Overall, the proposed data engineering scheme helps, as the primary step for data complexity reduction, to extract meaningful features and patterns, improving accuracy and reliability in bearing fault diagnosis and prognosis. Techniques like time-domain analysis, frequency-domain analysis, wavelet analysis, and statistical feature extraction help to distinguish among different types of patterns in sensor data of different fault types. The transfer learning and aggregation process leverages additional knowledge from diverse models trained on diverse conditions in other domains, which improves the accuracy and effectiveness of fault diagnosis and prediction algorithms. The LSTM adaptive deep learning capability effectively helps to follow data shifting, keeping the model always up to date and focused on new data. Thus, potentially, depending on the performance of the entire proposed data engineering methodology and CSIDTL, this approach could be applied to fault diagnosis, prognosis, and remaining-useful-life assessment, as well as data anomaly detection and mitigation. From the perspective of the aerospace industry, where reliability and safety are paramount, this coupled data engineering scheme within CSIDTL could significantly improve maintenance practices. Additionally, it could help identify potential bearing failures or anomalies, enabling timely maintenance and reducing the risk of critical malfunctions. Additionally, the real-time monitoring provided by CSIDTL fault diagnosis/prognosis could help optimize operational efficiency by minimizing downtime and increasing productivity. This methodology could also be extended beyond aerospace to other areas, such as automotive or industrial machinery, where bearing efficiency and performance are vital. As a result, the practical application of such methodology for real-world fault diagnosis/prognosis has great potential for improving asset management and safety in a variety industries. ### Comparison with Previous State-of-the-Art Works

As mentioned earlier in the section on research gaps, previous state-of-the-art works transformed datasets into less complex feature spaces by performing sub-sampling, selecting a few sets to illustrate the performance of their model, or merging different sub-classes of unhealthy modes to reduce the number of classes, thereby reducing the complexity of the problem to be solved. While doing so, most of the experiments discussed a simple random splitting of the dataset, while the prognosis case was not considered the main goal of their studies. As a result, these works clearly achieved great accuracy in this case. However, as far as generalization is concerned, their models lack generalization at some point compared with this study. In fact, this study is conducted on a way-too-massive feature space, with multi-class classification problems (i.e., seven operating modes) and different case-study scenarios (diagnosis and prognosis). Additionally, this paper treats the data unavailabilityproblem, drift, and complexity instead of only treating complexity as in previous works. Furthermore, this paper specifically introduces a well-structured data engineering scheme, justified by data challenges, that leads to outstanding data representation as the primary step to target data complexity.