## 5 Information Processing Module (IPM) Design
### Exploratory Data Analysis (EDA)  
The exploratory data analysis (EDA) process begins with gaining a clear understanding of the original dataset, including recognizing its features and target variables. The original dataset contains 12 numerical features (P\({}_{\text{inAC}}\), P\({}_{\text{inDC}}\), P\({}_{\text{outAC}}\), P\({}_{\text{outDC}}\), i\({}_{\text{inAC}}\), i\({}_{\text{inDC}}\), i\({}_{\text{outAC}}\), i\({}_{\text{outDC}}\), V\({}_{\text{outAC}}\), V\({}_{\text{outDC}}\), R, and D) and one categorical target (capacitor status). Next, it is important to consider whether any additional processing is needed, such as dealing with duplicate entries or missing values. In the original dataset, no missing values or duplicate records were found. However, Figure 7 suggests the presence of outliers in some features, which is why individual boxplots (Figure 8) were created for each of the features to better visualize their distributions. The preceding figure clearly highlights the presence of outliers in the features related to current and power. However, these outliers will not be removed, as the goal is for the proposed solution to assess the capacitor's condition even under extreme operating conditions. The performance of ML models is highly influenced by the features used for prediction. Therefore, selecting the most relevant features is crucial to optimizing the model's performance. Irrelevant features can increase the model's complexity, increase the computation  
Figure 7: Complete dataset used to train and test the machine learning models. time, and often lead to overfitting [51; 61]. Identifying and removing irrelevant features from the dataset is an essential step before starting the ML model training stage. The reduction of features is based on their relevance and redundancy in relation to the target. Thus, features can be classified as strongly relevant, weakly relevant but not redundant, irrelevant, or redundant [61]. Relevant features should be retained unless they are highly correlated with other equally relevant features. Although weakly relevant features may not contribute significantly to ML model learning, they will be preserved in this case as long as they are not strongly correlated with each other, for two main reasons: first, the total number of features is relatively small; the second is due to the fact that some weakly relevant features allow the model to identify the operating state of the converter, which is essential to avoiding incorrect classification of the capacitor condition, particularly under extreme operating conditions. In this context, it is important to highlight that PCs are intrinsically non-linear systems, which adds significant complexity and other challenges to the learning process of ML models. Consequently, it is crucial that the ML model reliably identifies the converter's operational state to ensure accurate performance. Irrelevant features, particularly those that do not help the ML model to identify the converter's operating condition or the capacitor's condition, as well as redundant features, will be removed from the final dataset. To evaluate the redundancy among two features, X and Y, the Pearson correlation coefficient was computed as follows [51]:  
\[r=\frac{n\times\Sigma\ (X\times Y)-\sum X\times\sum Y}{\sqrt{(n\times\sum X^{2}-( \sum X)^{2})\times(n\times\sum Y^{2}-(\sum Y)^{2})}} \tag{1}\]  
where n represents the number of samples (7200). Figure 9 illustrates the correlations among all features. Figure 8: Individual boxplots regarding the features. For this problem, a maximum correlation threshold of 0.95 was established to identify pairs of redundant features [62; 63].