###### Abstract
For instance, the industry has reported that switching from a metallic grid coupling to a jaw coupling can alter the signature of a misaligned rotor system in the frequency domain. The effects of uncertainties in the analysis of rotor systems have been the subject of numerous studies in recent years [26]. [PERSON] et al. [25] quantified the impacts of uncertainty in rotating systems with multiple faults. The quantification used the Harmonic Balance technique, and the results were compared to those calculated using Monte Carlo simulations. Recent years have experienced noticeable growth in the use of wavelet frameworks for noise reduction, wavelet transformation, and wavelet time scattering (WTS) [27]. In the feature extraction stage, wavelet transform and WTS are useful tools [28]. The continuous wavelet scalogram can also function as the visual input for feature extraction in a convolution layer [29]. Experimentally, [PERSON] and [PERSON] [30] applied discrete wavelet transform and a multi-layer artificial neural network in the classification of healthy and cracked rotor systems in various crack depth scenarios. Relative wavelet energy (RWE) and wavelet entropy (WE) were extracted at the different levels of decomposed signals and then were fed into the perception learning network. Wavelet time scattering is a suitable feature extractor as well as the phase before exploring fault indicators because they maintain translation invariance, deformations, and high-frequency information [31]. Numerous studies have established the ability of WTS in feature extraction of rotating devices. [PERSON] et al. [32] employed the wavelet scattering transform of acoustic emission in the classification of four faults in a gearbox. In the training phase, the system was not exposed to all loads, but the benchmark could still separate classes. To diagnose bearing faults in rotor systems, [PERSON] et al. [33] used WTS; the suggested approach was also contrasted with three feature extraction and prior fault detection benchmarks. The implementation of smart processes for manufacturing, enhancing quality, and fault diagnosis has significantly advanced, thanks to artificial intelligence, cloud computing, and the Internet of Things throughout the Industry 4.0 revolution [34]. The application of intelligent techniques in condition monitoring and fault diagnosis of rotating systems is witnessing a noticeable improvement during the revolution [35]. It is well known that using such a method has both benefits and disadvantages. Difficulty in the physical comprehension and interpretation of signals is one of the drawbacks of smart fault diagnosis procedures, and this becomes particularly challenging when multimodal behavior is attained. On the other hand, the noticeable benefit of such techniques is their capabilities in real-time condition monitoring and fault detection. Using intelligent methods can also diminish human error when a wide range of classes are under process, or a decision must be taken instantly. A fault diagnosis model for rotor systems was proposed by [PERSON] et al. [36] which combines data-driven and failure mechanism analysis. The model is based on vibration signal feature vector transfer learning, with the Relief algorithm for feature extraction and the WkNN classifier for reducing data set differences. The model was tested using real fault data from multiple machines and demonstrated good generalization and accuracy in cross-device diagnosis. With accurate and efficient calculation, the results have industrial application value. [PERSON] et al. [37] improved fault detection in the bearing of rotary machines by using a combination of vibration and acoustic signals. Simulated faults were created in a bearing, and a database was generated to compare the signals. Three different supervised machine learning methods were proposed and tested, resulting in a fault classification above 96%. The results present a new approach to enhance predictive maintenance with vibration and acoustic signals.