###### Abstract
An explainable model and interface were proposed for PdM applications with a public dataset that reflects the real predictive maintenance encountered in the industry [23]. In paper [23], after analyzing the dataset [24], the classifier was trained, its performance was measured, and finally, the explanations provided by an explainable model and interface were evaluated. In another publication using the same PdM dataset, an approach for self-adaptive cyber-physical systems was proposed in the literature [25]. In the last part of their paper, a case study was conducted with a specific focus on the fault prediction module within the cyber-physical systems approach they proposed. Within the scope of this study, classification was performed to estimate the fault diagnosis (machine failure and non-failure) using only three features of the dataset. Finally, clustering algorithms were applied to the dataset, and the clustered fault types were displayed in 3D graphics. The discussion section in our study provides detailed accounts of other studies in the literature utilizing this dataset, including a comparison table with explanations and performance metrics. In our study presented in this paper, we used the same dataset constructed with the aim of reflecting real predictive maintenance data in the industry [24]. The dataset contains 10,000 data points and it can be defined as an imbalanced dataset since only 3.4% of points represent fault data. The general architecture of our study is presented in Figure 1 as a flowchart diagram. In this paper, essentially, shallow classification techniques were applied for general machine failure and other fault type labels in the imbalanced dataset. Unlike the studies in the literature, the classification performances were examined with deep learning architectures considering the features included in the dataset as a one-dimensional vector. Then, simulations were carried out on the dataset after feature normalization, the classification algorithms were repeated, and the performance results were recorded. Eventually, the balanced datasets were generated for each fault type over the whole dataset. The study was completed by comparing the performance metrics by applying shallow and deep learning classification models on the imbalanced and balanced datasets with and without feature normalization. In short, this study unfolds through the following pathways, and can be summarized as follows:  
* Within the scope of classification studies, in addition to shallow machine learning algorithms, one-dimensional deep learning architectures (1D-LeNet, 1D-AlexNet, and 1D-VGG16) were also used for PdM. * Before fault classification, the data preprocessing operation mentioned in the \"Dataset Description and Preprocessing\" section was applied to the original dataset to remove outliers, unlike the studies using the Matzka PdM dataset. * Feature normalization techniques such as min/max and z-score, which were not investigated in the Matzka PdM dataset, were used to ensure that the features were on a similar scale and had the same range of values. * To demonstrate the impact of imbalanced and balanced distributions of labeled data on classifier performance, new dataset groups were generated for all fault types; generations were performed with 100 different repetitions and the maximum performance scores are displayed in tables, diverging from the analyses conducted with the Matzka PdM dataset. * A table was constructed to compare this study with others in the literature that used the same original PdM dataset, focusing on methodology, dataset type, fault type and performance metrics.