## 3 Materials and Methods  
### Background  
The first approach for a given problem with multiple related parameters is to create a model to solve it. However, the problem might arise with a high level of complexity to formulate a model for several reasons: the problem might inherit non-linearly correlated parameters, or there has not been enough research or attempts to produce reliable models to experiment on. However, if there is information-rich observation data, machine learning algorithms can be used to create models with little or no human intervention [45]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Main Focus** & **Finding** & **Method** & **Dataset** \\ \hline
[5] & Vibration analysis in turbomachines using ML. & ML can be effective for vibration analysis in turbomachines. & Machine learning techniques. & Real dataset \\ \hline
[7] & To detect 10 different faults in rotating machinery. & Classification with an accuracy of 99.8\%. A total of 11 key features were identified. & SVM, decision tree, and dimensionality reduction & Real dataset \\ \hline
[9] & To diagnose bearing faults. & Fault types were determined using CNNs and LSTM networks. & CNN and LSTM combination & Real dataset \\ \hline
[19] & To detect motor faults early using a synthetic dataset. & The model tested on a synthetic dataset showed similar accuracy to real datasets. & Synthetic dataset creation, CNN, and SVM & Synthetic dataset \\ \hline
[40] & To detect faults in dry friction clutch systems. & The highest accuracy was obtained with the Random Forest and kNN. Classifier combinations performed better. & Random Forest, kNN, and voting strategy & Real dataset \\ \hline
[41] & To overcome the lack of labeled fault data by using semi-supervised anomaly detection. & Traditional methods performed similarly or better than deep learning-based methods. & Semi-supervised anomaly detection & Real dataset \\ \hline
[42] & To predict wind turbine motor faults using synthetic data. & Predictions on synthetic data showed high accuracy for wind turbine motor faults. & A novel deep learning approach and DC-CNN. & Synthetic dataset \\ \hline
[43] & To predict motor maintenance times using synthetic fault data. & Predictions based on synthetic data were close to those made using real data. & Synthetic dataset and regression models & Synthetic dataset \\ \hline
[44] & Fault detection and classification in rotating machines & Success of the RF algorithm and the importance of kurtosis and ultrasound levels & kNN, SVM, RF, and various boosting techniques & Real dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of studies on motor fault detection using real and synthetic datasets. Machine learning can be classified into three categories: supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning does not operate on labeled datasets but identifies hidden patterns in the data by optimizing a measure of merit [36; 46; 47]. Reinforcement learning trains models to make sequential decisions by rewarding them through trial and error. Supervised learning is based on training models on labeled datasets and making predictions based on input training data [48]. Input data may be numeric, categorical, textual, etc., and the output data are either continuous values (regression) or a class label (classification). A classification task involves separating the data into two datasets: a training set and a test set. Each data point in the training set contains several attributes and a target value (label). The goal is to build a model that can predict the target value of the given data with the attributes the model is trained on. The success of the classifier is quantified by validating the test data and presenting it using the performance metrics. To determine which classification method to use, the best way is to follow a trial-and-error approach [49].