###### Abstract
In the present-day context, many efforts have been made to improve the performance of intelligent diagnostic systems. The attention given to certain methods, such as convolutional neural networks (CNNs), is due to two main reasons: improved accuracy and automated feature extraction. CNNs have been shown to deliver high accuracy in different applications, such as image classification, object detection, and natural language processing. Additionally, they can automatically learn and identify important features from the data, eliminating the need for manual feature engineering which can be time-consuming and prone to errors, outperforming other methods [38; 39]. [PERSON] et al. [40] classified unbalanced, misaligned, and cracked rotor systems using CNNs and classic machine learning algorithms. Inputs for the CNNs were colorful persistence spectrums, while on the condition of classic techniques, statistical features were extracted directly from the raw data. [PERSON] et al. [41] classified a rotor system that was simultaneously experiencing shaft bow and unbalancing using an artificial neural network (ANN) and wavelet transform by analyzing the vibrations in the two transverse and axial directions. The signals were split up using a discrete wavelet transform, and during the feature extraction process, the root means square (RMS) values of the detail coefficients were used. The training set consisted of three scenarios (different values of mass unbalance and initial bow magnitude), and one was used in the testing phase. Recurrent neural networks, more specifically long short-term memory (LSTM), have established themselves as powerful algorithms in time series data analysis in recent years. Similar to CNNs, one can set feature extraction layers inside the structure of such algorithms. Moreover, their capabilities in the usage of the graphics processing unit (GPU) can be considered an extra merit. Numerous investigations have demonstrated fault diagnosis in rotor systems using LSTM. In an experimental setting, [PERSON] et al. [42] applied LSTM to the fault classification of gears in the rotating part of wind turbines. [PERSON] and [PERSON] [43] studied the capability of LSTM in the classification problem of rolling element bearings in rotor systems. Support vector machine (SVM) models' prowess in categorizing damaged rotor systems has been extensively studied. Using supervised and unsupervised algorithms, SVM models can handle labeled and unlabeled data sets [44]. When the supporter ball bearings were also damaged, [PERSON] et al. used ANNs and SVM in the theoretical and experimental classification of healthy and cracked rotor systems. To extract features and reduce dimensionality, statistical operations were applied to the vibration signals [45]. SVM was employed by [PERSON] et al. [46] to categorize healthy and unbalanced rotor systems. The features of the time and frequency domains were compared; it was claimed that the time domain features that were extracted could produce results that were more accurate overall. Since the sign of parallel and angular misalignment in a rotor system is the same, that is, 2X frequency harmonic, [PERSON] et al. [47] used the SVM model to discriminate between these two conditions. Time domain features were extracted from the vibroacoustic signals, e.g., the fusion form of the vibration and acoustic signals. In the context of this study, combinations of WTS with LSTM and SVM are employed in the classification of unbalanced and bowed rotor systems. The process of modeling is performed using ABAQUS/CAE, and the modeled systems are simulated when the value of Young's modulus is not constant which can result in changes in the mass and stiffness matrices of the whole system. Variation of the aforementioned parameter is applied utilizing uncertainty where the perturbation method is responsible for producing different magnitudes. Additionally, a data set is produced by altering the rotational velocity in each Young's modulus scenario.