###### Abstract
With the PdM applications made in this way, the aim is to minimize the number of unscheduled failures and outages, reduce the cost of maintenance, and improve the efficiency of machinery [9; 10]. PdM seeks to determine optimal maintenance parameters by analyzing fault classification results, which involves identifying the root cause of failures and categorizing them based on priority and potential impact. In recent years, various types of machine learning methods have been applied for analyzing and classifying the faults. In the literature, a Support Vector Machine (SVM) algorithm for diagnosing induction motor faults was presented using frequency-domain vibration signals [11]. In another study, wavelet transform (WT), a well-known time-frequency domain analysis tool, was used to diagnose and prognose bearing faults [12]. As another example, artificial neural network techniques and fuzzy integral were considered for the fault diagnosis of large-scale power systems [13]. In addition to these studies, by applying feature extraction using wavelet and applying classification using the decision tree (DT) machine learning algorithm, fault diagnosis of the monoblock centrifugal pump was made with vibration data [14]. In a paper [15], a logistic regression classification method which applied to a labelled oil analysis dataset was used to classify machine conditions, e.g., healthy or faulty. In the literature, there are different studies which have been conducted in the field of PdM, such as bearing fault detection and bearing degradation performed by shallow machine learning algorithms [16; 17]. [PERSON] et al. [18] presented a machine learning model with two different algorithms such as SVM and multi-layer perceptron for early fault prediction of a centrifugal pump in the oil and gas industry. [PERSON] et al. [19] used machine learning techniques for analyzing vibration signals and feature extraction to classify the wear level of bearings, essentially aiming for fault identification and classification. In view of the fault diagnosis of industrial process equipment, the authors introduced a convolutional neural network identification method with continuous wavelet transform on the vibration signal of the equipment and achieved good diagnosis results [20]. [PERSON] et al. suggested a diagnosis study based on machine learning which combined the data self-generation and convolution neural network methods for classification of typical chiller faults [21]. In Ref. [22], the authors proposed a k-Nearest neighbor (k-NN) hierarchical clustering method in their paper for dynamic ensemble selection in Random Forests to solve fault classification problems, which are a significant part of industrial process. While these studies contribute valuable insights, there are limitations and gaps in the existing literature. In particular, many studies are based on shallow machine learning algorithms. Additionally, data imbalance issues persist and affect the performance of fault classification models. Despite these contributions, existing work often faces challenges such as limited scalability, suboptimal performance due to data instability, and reliance on shallow machine learning methods. Our work aims to address these challenges by introducing novel methodologies for fault classification in industrial environments, developing an efficient approach for automatic fault classification by leveraging data balance and normalization techniques, and conducting a comprehensive analysis comparing one-dimensional deep learning methods with shallow machine learning-based approaches. Most of the PdM studies cited in the literature that we mentioned above have utilized data from experimental setups, with very few relying on real-world data. There are several challenging estimation and classification problems that need to be solved associated with PdM in the industry. Mostly, it is difficult and almost impossible to obtain real industrial data in this area. Therefore, synthetic data obtained from real or software simulations are used in the literature.