## 4 Discussion  
In the literature, many studies about fault diagnosis have been carried out with shallow classification methods. Some of these studies, which use the same dataset reflecting real PdM data [24], are presented in Table 6, along with their classification methodology, dataset type, fault type, and performance metrics. [PERSON] [23] implemented an explainable model with an interface for PdM applications. A confusion matrix was generated from the results of the binary classification for the machine failure label conducted with the bagged trees ensemble classifier by applying the 5-fold cross-validation method. The accuracy and F1-score of the study were calculated as 98.34% and 77.98%, respectively. In the study, an original imbalanced dataset was used without any data preprocessing operation and without feature normalization. The data-blind machine learning (ML) framework presented in paper [48] achieved 97.30% accuracy on the imbalanced original dataset, focusing only on the machine failure class, and used a data-blind ML algorithm without any data preprocessing, a departure from our approach outlined in this study. Random undersampling boosting (RusBoost), GradientBoost (GB), Extreme gradient boosting (XGBoost), and Categorical Boosting (CatBoost) are boosting methods which struggle with imbalance. In studies in the literature, these models have been applied to the same dataset. Four papers using these algorithms reached 92.74% accuracy, 45.90% F1-score [49], 94.55% accuracy, 49% F1-score [50], 95.74% accuracy [51] and 88.83% accuracy [52] scores, respectively. When we compared our study with these boosting techniques, our method was found to outperform these models. For example, our paper (98.50% accuracy) demonstrated its superiority on the same predictive maintenance dataset. In one of the other studies in the literature, the authors present a machine learning -based approach for industrial machine failure and they achieve the highest performance by using the DT classification method among the non-deep learning algorithms [53]. The selected machine learning method (DT) reached max. 77.66% F1-score values in paper. In addition to these methods, hybrid studies have used unsupervised and supervised machine learning (HUS-ML) classification algorithms. From these studies, the authors achieved the highest accuracy of 98.46% and F1-score of 78.80% [54]. Our study, including both SML and DL classification algorithms alongside data preprocessing, provides a more comprehensive analysis of not only machine failure class but also all other fault types in the dataset. Another basic approach in fault classification involves the studies using a Random Forest (RF) model, which is one of the ensemble learning techniques [55]. The authors achieved 98.40% accuracy in their results with a dataset which was divided into two parts, such as 80% train and 20% test, without cross-validation. However, in this particular study, only shallow machine learning approaches were employed, with no cross-validation conducted. In contrast, we implemented a 5-fold cross-validation approach. In another study in the literature [56], an approach for a mathematics-based data quality assessment of the synthetic industrial dataset [24] was proposed. They suggested a form of mathematical modeling, which is called a binary logistic regression (BLR), to be carried out for data quality evaluation verification and to solve binary classification problems. The researchers obtained the classification results as follows: accuracy was 97.10% and F1-score was 44.07%. The study was conducted with an imbalanced dataset using only machine failure class without normalization and data preprocessing.