###### Abstract  
Predictive maintenance (PdM) is implemented to efficiently manage maintenance schedules of machinery and equipment in manufacturing by predicting potential faults with advanced technologies such as sensors, data analysis, and machine learning algorithms. This paper introduces a study of different methodologies for automatically classifying the failures in PdM data. We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector Machines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. We reapply classification algorithms to the normalized dataset and present the performance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset groups by randomly selecting normal data and all faulty data for all fault types from the original dataset. The dataset groups are generated with 100 different repetitions, recording performance scores for each one and presenting the maximum scores. All methods utilized in the study are similarly employed on these groups. From these scores, the use of 1D-LeNet deep learning classifiers and feature normalization resulted in achieving the highest overall accuracy and F1-score performance of 98.50% and 98.32%, respectively. As a result, the goal of this study was to develop an efficient approach for automatic fault classification, leveraging data balance, and additionally, to provide an analysis of one-dimensional deep learning and shallow machine learning-based classification methods. In light of the experimentation and comparative analysis, this study successfully achieves its stated goal by demonstrating that one-dimensional deep learning and data balance collectively emerge as the optimal approach, offering good prediction accuracy. predictive maintenance (PdM); fault classification; data balance; shallow machine learning; one-dimensional deep learning; balanced dataset; imbalanced dataset; feature normalization +
Footnote †: journal: Medical  
them to produce complex products in shorter times and with higher quality by integrating into production facilities the fourth industrial revolution, or Industry 4.0. The goal of Industry 4.0 is firstly to make factories and machines self-aware and enable them to gain the ability to make autonomous decisions and self-control about production, assembly, and maintenance [2; 3]. One of the most important outcomes of Industry 4.0 is predictive maintenance (PdM), which is an improved digital version of machine maintenance. Key benefits of PdM can be summarized as follows: the detection of abnormal machine behavior, prediction of failure modes, improvement of machine control, and high product quality [4]. In addition, it contributes to reducing the risks of machine failures, extending the life of machinery, optimizing maintenance time, and the use of spare parts [5; 6]. PdM and real-time analysis of equipment situation information, e.g., failure, normal, or healthy state, is usually obtained from sensors on the equipment and/or external measurements taken by portable devices, and then all this measurement data are processed via software [7]. In order to achieve healthier and more successful results in PdM, it is necessary to collect sufficient numbers of temperature, vibration, noise, lubrication, and corrosion signals from the machine in the production process [8]. A neural network can be trained with these signals, which come through sensors on the equipment. Machine performance can be monitored, and predictions about future situations can be made by using an artificial neural network model.