## 2 Dataset Description and Processing
### Illustrative Example of Processed Data
#### 2.4.1 Variable-Speed and -Load Set  
Regarding the variable-speed and -load test, since data scatters are very numerous, we decided to take some interesting examples of load and speed to be able to observe their effect on data complexity. Accordingly, four conditions, namely, 1, 4, 8, 13, with nominal speed (Hz) and voltage load (mV) of {(100, 0), (100, 900), (300, 500), (500, 500)} corresponding to the qualitative variables of {(minimum speed, minimum load), (minimum speed, maximum load), (average speed, maximum load), (maximum speed, maximum load)) are selected. The reason for selecting only these conditions is that the dataset is massive and contains a wide range of subsets and classes, which cannot be illustrated at once. Therefore, these examples are considered very adequate to obtain a general conclusion on the whole dataset, since they contain information on almost all possible cases of variations in load and working conditions. In our example, we provide rounded values for load voltage with sensitivity of 0.499 mV/N (see first paragraph, page 6 in [7]).  
In this context, t-Distributed Stochastic Neighbor Embedding (t-SNE) is used to map high-dimensional data to a low-dimensional space while preserving similarities among data points, with emphasis on distances relative to each other. As a result, the data scatters in Figure 6 are obtained to visualize and explore our dataset. Figure 6a refers to healthy operating modes, low distortions, and less harsh working conditions. There is minimum speed and null load, and the bearings are completely healthy. In this case, it can be observed that samples of different data classes have some pattern agglomeration of different operating modes (e.g., class 4 and class 2) that can be easily distinguished even with a simple linear model. On the other hand, comparing these representations with each other or with other classes clearly explains the complexity of the data, reflected by higher cardinality. Contrariwise, the prepared version in Figure 6e increases this classification capability by showing an additional number of distinguished classes (e.g., 0, 4, 2, 6). This clearly illustrates the efficiency of data processing, especially denoising and removing outliers. It should be mentioned that these details only appear in two-dimensional t-SNE distributions. Thus, it is possible to observe better performance of the proposed data engineering details if we explore three-dimensional data visualization. The same explanation could be projected onto Figure 6b-d when comparing them to their prepared version in Figure 6f-h. This clearly shows that data engineering helps to reveal some important patterns in the data that are hidden in the original feature spaces. Finally, further mapping processes using CSITDL could improve representations of feature spaces.  
Figure 6: Comparison between raw and processed data scatters of variable-speed and -load experiments: (**a–d**) raw data scatters of conditions 1, 4, 8, 13, respectively; (**e–h**) prepared data scatters of conditions 1, 4, 8, 13, respectively.  
So far, presentation and argumentation are based on comparing dataset scatters of raw data and prepared data. However, to obtain a clear conclusion about data characteristics beneficial for the next stages of adaptive deep learning, another important note in this case can be mentioned. We can observe, in Figure 6e-h, that the harsher the conditions are (i.e., greater speed and load), the more they lead to numerous distortions. This further complicates the process of understanding and distinguishing different kinds of patterns, increasing the level of cardinality, which definitely increases the complexity of the deep learning process.