## 3 Experimental Results
### Performance Metrics for MF Class Using Imbalanced and Balanced Dataset with Feature Normalization  
The normalization operation, which is one of the preprocessing techniques, was applied to ensure that all the features included in the imbalanced and balanced datasets had an equal impact on the proposed classification models. First of all, for the machine failure label, min/max normalization and z-score normalization were performed on the features found in the imbalanced dataset and then the shallow and deep learning classification algorithms were applied on the new normalized dataset, separately. Performance metrics were calculated individually for min/max and z-score normalization. The maximum performance values of these two normalization results were added to the existing Table 4 to prevent confusion caused by an overflow of data. When analyzing the performances of the SML models, it is evident that both DT and SVM models possess higher recall and precision compared to others. Specifically, the DT model achieves a recall rate of 23.64% and a precision rate of 82.98%, while the SVM model attains a recall rate of 19.39% and a precision rate of 86.49%. Conversely, the k-NN model shows a higher recall rate of 45.45% but a lower precision rate of 57.47% when compared to DTs and SVMs. In terms of deep learning architectures, such as 1D-LeNet, 1D-AlexNet, and 1D-VGG16, they generally display relatively similar performance metrics compared to SVM models in the imbalanced dataset with feature normalization. For instance, both 1D-AlexNet and 1D-VGG16 demonstrate higher accuracy and F1-score rates at 97.70% and 50.75%, and 97.75% and 52.14%, respectively, than the SML models. Secondly, for the MF label, the balanced dataset was generated according to Table 3. The same approach as the previous study was performed again on the balanced dataset and performance results are given separately in Table 4. After the normalization process, performance metrics in Table 4 show improvement for most of the SML algorithms and all DL architectures compared to the studies conducted in the first two sections of the experimental results. For instance, DT achieves a recall rate of 94.55% and a precision rate of 95.71% after normalization and balancing of the dataset. Similarly, SVM demonstrates a recall rate of 98.18% and a precision rate of 94.19%. Even the k-NN model, which already showed high performance, sees further improvement with a recall rate of 93.64% and a precision rate of 98.72%. Moreover, as can be seen from Table 4, the highest classification performance values were obtained in DL classification algorithms such as 1D-LeNet, 1D-AlexNet, and 1D-VGG16 using a balanced dataset with feature normalization. These models achieved remarkable recall rates of 97.27%, 96.67%, and  
and DL classification algorithms were separately investigated on a min/max-normalized and z-score-normalized dataset. While preparing the Table 5, the maximum performance results from two normalizations were utilized, min/max and z-score, to avoid confusion stemming from appearing data. Table 5 contains the results of experiments conducted with a balanced dataset without normalization for each fault type (HDF, OSF, PWF, and TWF), indicating that shallow classification methods achieve high values in critical performance metrics such as precision, specificity, and F1-score. In addition, as can be seen from Table 5, the one-dimensional DL models achieved higher classification performance values compared to the shallow classification models when trained on a balanced dataset with feature normalization for each fault type (HDF, OSF, PWF, and TWF). Additionally, the results demonstrate that the 1D-LeNet model achieved the highest classification performance values among all the models when trained on a balanced dataset with feature normalization for each type of fault. Figure 8: Performance metrics comparison for different models on imbalanced dataset with feature normalization.