## 3 Methods, Application, and Result Discussion
### Proposed Approach
More specifically, the LSTM network is considered a robust generalizer only when data are processed under less harsh conditions, as it is seen for conditions 3, 6, and 8 in Figure 9a. Contrariwise, the CSIDTL showed its performance on the entire subset. This means that the adopted approach of selecting and aggregating the best learners and transferring them across conditions better helps to overcome the lack of representative data and not only benefits the data drift mechanism of LSTM. Figure 9: Comparison of classification performance of studied models: (**a**) LSTM network; (**b**) LSTM network with CSIDTL learning methodology. Table 3 introduces the best training models per round under each specific condition. It is clear that the load had an impact on the training process more than rotational speed. This is why the harsh conditions highlighted in green color show that the model needed further and deeper representations (i.e., CSIDTL) than ordinary deep learning (i.e., LSTM). The models also expressed their need for additional information across working conditions, leading to consuming more learning time to reach a better data representation. This fully confirms our initial perception: _\"learning under data unavailability, complexity, and drift requires generative modeling, adaptive learning, and deep learning all together in a global learning process\"_. #### 3.2.2 Endurance Experiment

For the endurance experiment, the best learning parameters obtained (Equations (1) and (2)) from the fifth round of the first experiment were transferred as they were to the learning process dedicated to prognosis. In this case, a single subset of three different classes related to different damage severity related to bearing health conditions was treated. The same learning CSIDTL options were kept in this case. The only thing that changed was the number of training rounds. So, the models seemed to have achieved great results in round 1 (see Table 4) in this case (meaning round 7 if we count the first five rounds in the first experiment resulting in weight initialization in the second experiment). Similarly, in this case, there were 3 \(\times\) 3 confusion matrices, which are difficult to be illustrated in this case. Instead, the averaged results are showcased in Figure 10. Accordingly, after the learning process, which took about 0.1754 h, the learning performance was improved by about 10.87%, reaching about 95.65% accuracy, which is considered a great achievement for a very complex dataset with a higher level of complexity and drift. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Speed (Hz)** & **Load Voltage** & **Best Round** & **Elapsed Time** (h) \\ \hline
1 & - & - & 1 & 0.1754 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best learners per rounds. Figure 10: Results obtained in the endurance test dataset for each round. \begin{table}
\begin{tabular}{l c c c c c c c c c c|c c c} \hline \hline
**Condition** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline
**Speed (Hz)** & 100 & 100 & 100 & 100 & 200 & 200 & 300 & 300 & 300 & 400 & 400 & 500 \\
**Load voltage (mV)** & 0 & 500 & 700 & 900 & 500 & 700 & 900 & 500 & 700 & 900 & 500 & 700 & 500 \\
**Best round** & 1 & 1 & 0 * & 5 & 3 & 3 & 0 * & 0 * & 1 & 4 & 3 & 4 & 4 \\
**Elapsed time (h)** & 0.9237 & 0.9237 & 1.1060 & 0.7888 & 1.1097 & 1.1097 & 1.1060 & 1.1060 & 0.9237 & 1.0546 & 1.1097 & 1.0546 & 1.0546 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best learners per rounds. ### General Discussion

In general terms, the problem addressed in the case of the two experiments shows the following:

1. The need to transfer learning across conditions to improve classification performance in terms of the generalization of the learning model (Figure 9 and Table 3);
2. CSDTL better expresses the relationships between the number of rounds required for training and data complexity to overcome data drift problems.