## 2 Materials and Methods
### Classification  
Classification as a subfield of artificial intelligence can differentiate between two or more categories based on the extracted features. Thanks to the state of the art in this field, a machine learning (ML) network and its subfield, a deep learning (DL) network, can be used to perform intelligent tasks, such as classification, regression, and anomaly detection. The number of node layers--or depth--of ML and DL networks is the key distinction: In a deep network, the number of hidden layers ought to be two or more [56]. Utilizing recurrent neural networks (RNNs) such as LSTM and convolutional neural networks (CNNs), deep learning can be performed on sequence data and time series data and visual objects (such as images). With memory cells and recurrent connections, LSTM is a feedforward neural network (nodes do not form loops). As an optimized neural network algorithm, LSTM manages long-term dependencies (with the LSTM layer), explores gradients, and handles vanishing problems. There are three various gates in an LSTM, i.e., input, output, and forget that have the responsibility of conducting information throughout the network in such a way that the network decides which information is kept and which to be discarded through the gates. This property gives LSTMs further capability for remembering information rather than the other types of RNNs. An LSTM network normally consists of various layers, which include an input layer, one or more LSTM layers, an output layer, and optionally, fully connected layers. The entry point for data is the input layer while the LSTM layers are fundamental layers containing the LSTM cells, which handle the input data and preserve the network's internal state. The output layer receives the output from the LSTM cells and generates a prediction of output. Fully connected layers can be used for classification or regression tasks and map the output of the LSTM layers to a specific output size or range. To enhance the network's ability to learn complex patterns, an activation layer can also be added to introduce non-linearity. It should be noted, however, that the number of layers, LSTM cells, and fully connected layers may need to be adjusted based on the specific task and data set to achieve optimal performance [57, 58]. One of the most popular supervised machine learning models, support vector machine (SVM) as non-parametric model has demonstrated its effectiveness in both classification (SVC) and regression (SVR) tasks. Along with its solidity against model errors, the technique's fundamental advantage is its ability to handle a small number of features with reasonable accuracy as well as explore non-linear dependencies in the data [59]. SVM can be performed in a data set where features of two or more classes are distinguishable in a linear also non-linear approach using the optimal hyperplane (the separation plane). A kernel made up of a set of mathematical functions can be employed to modify the input data when the condition indicators of classes are non-linear. There are several types of kernels, such as Dirichlet, Sigmoid, radial basis function, polynomial, and Gaussian. The polynomial kernel itself can be divided into cubic, quadratic, etc., based on its order [60, 61]. This work utilizes LSTM as a supervised deep network and SVM as a supervised machine learning algorithm to categorize faulted systems into Class 1 and Class 2 (with indices 0 and 1 as the labels in the data set for the unbalanced and bowed rotors, respectively). These two classification models were chosen because of how well they each do at determining the connection between the input data and the intended response. While SVM can identify non-linear reliance, LSTM was developed to reveal short- and long-term subordinations. However, it should be noted that the entire procedure was examined by fewer number of observations, and the employed numbers are the lower required limit for the current use.