## 4 Discussion
In other studies which were implemented on the same dataset, researchers reached good performance metrics of classifiers for the prediction of machine failures with state-of-the-art methods, e.g., Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Clustering by Fast Search and Find of Density Peaks (CFSFDP--63% F1-score) [57], Evolving Fuzzy Neural Classifier with Expert rules (EFNCxp--97.30% accuracy) [58] and a Data Filling Approach based on Probability Analysis in Incomplete Soft sets (DFPAIS--83.74% accuracy) [59]. The results were directly sourced from related papers in which the authors used the same PdM dataset [24] as that used in our current study. However, unlike our study, these studies do not include data preprocessing andcleaning operations. Furthermore, our study encompasses all fault types in the original PdM dataset, enabling a performance comparison of the effects of data balancing using DL and SML. Our study was carried out by using SML and DL classification algorithms on both an imbalanced and balanced dataset after data preprocessing operations on an original PdM dataset. In the classification performance results on the imbalanced dataset for the failure and non-failure labels, high accuracy was observed alongside relatively low F1-score values across all scores, with SML algorithms notably outperforming DL. Due to achieving higher performance on the F1-score, we employed a generation operation that involved creating 100 different balanced datasets. In each repetition, non-failure data were randomly selected to ensure a balanced representation. Subsequently, fault classification was conducted on each dataset, with the maximum results recorded from these iterations. Since the ranges of features are very different in the used dataset, feature normalization methods such as min-max normalization and z-score normalization were applied. It was observed that feature normalization moderately increased the performance of the classification performance metrics in SML and especially DL classification methods. In this paper, considering the machine learning-based approaches, it can be said that classification performance results obtained from balanced datasets have higher overall performance values than imbalanced ones. To summarize, it is seen from Table 6 that 1D-LeNet, 1D-AlexNet, and 1D-VGG16 algorithms from DL classifiers reached better performance results compared to SML approaches such as DTs, SVMs, and k-NNs in this study. Figure 10 shows the confusion matrices for PdM fault classification using SML and DL models, illustrating the highest classification performance achieved by each model. Additionally, Figure 11 presents the ROC curves and AUC values for SML and DL models, demonstrating the highest classification performance achieved by each model. In future studies, we will apply data augmentation techniques to the original PdM dataset. In addition to binary classification, multi-class classification will be conducted to classify different fault types present in the original PdM dataset, and their performance will be examined. Also, in future studies, one-dimensional predictive maintenance data will be transformed into two-dimensional images and fault classification will be performed on these images using different deep learning algorithms.