## 1 Introduction  
Rotating machines find widespread utilization across diverse domains, including in the manufacturing sectors, wind energy generation, aviation propulsion systems, electric motor assemblies, marine propulsion systems, and mining equipment, owing to their notable attributes of elevated efficacy and robustness. Within the framework of Industry 5.0, a significant transformation can be observed, whereby the convergence of cutting-edge technologies and the promotion of sustainable practices in the manufacturing sector are emphasized. In this context, the utilization of rotating machines assumes a crucial and prominent role. Advancements in cutting-edge technologies in the field of material sciences have led to the development of rotating machines that are both faster and lighter, while also being capable of withstanding prolonged periods of operation. Numerous operational irregularities, including but not limited to mass imbalances, bowed shafts, cracked shafts, misalignment, elevated temperatures, quick acceleration, and frequent load fluctuations, all contribute to reduced efficiency and premature mechanical breakdown. Consequently, these issues result in unplanned periods of inactivity and financial detriment. Hence,ensuring the efficient maintenance and accurate fault detection and diagnosis (FDD) of rotating machines holds significance in achieving the objectives of Industry 5.0, whereby the convergence of efficiency, sustainability, and productivity is emphasized [1]. Vibration fluctuations, which are highly sensitive to both minor structural variations and changes in the operational process, are the basis for condition monitoring and diagnostics in rotating equipment. Any fault that occurs in a rotor will alter its vibrational behavior, and the degree of this impact depends largely on the various fault types. Given this, vibration-based diagnostics (VBD) has gained popularity and is often used in practice to diagnose a variety of rotor faults [2]. [PERSON] et al. [3] reviewed the recent advances in the VBD and prognosis of rotors with eight common problems. The approaches for fault diagnosis can be broadly divided into three categories: (1) physics-based or model-based approaches; (2) data-driven approaches; (3) the combination of various approaches, called the hybrid method. Data-driven approaches to rotor diagnosis utilize statistical theories or artificial intelligence (AI) algorithms to process the collected data, especially fault data, for fault diagnosis [4]. In recent years, data-driven approaches have become promising with the advancement of signal processing techniques such as empirical mode decomposition (EMD), Hilbert-Huang transforms (HHT) [5; 6; 7], and wavelet transforms [8; 9; 10]. In the foreseeable future, data-driven methodologies combined with artificial intelligence (AI) technology will remain well-liked and fruitful. Given the progress in the field of vibration analyses, there is increasing interest in the application of AI techniques for fault diagnosis in rotating machinery. The integration of AI approaches has gained significant recognition and traction in both academic and industrial circles, presenting a promising avenue for addressing industrial challenges. [PERSON] et al. [11] proposed automating the localization of imbalanced faults using ANN techniques. [PERSON] et al. [12] proposed a method for diagnosing faults in rotating machinery using a frequency domain vibration analysis and neural network (NN) pattern classification. A variety of unbalanced fault types have been localized with high precision on dynamic rotor test rigs using ANNs, providing benefits by reducing the number of sensors used. [PERSON] et al. [13] provided a comprehensive analysis of the AI-based research and development for rotating machinery fault diagnostics, encompassing both theoretical and practical standpoints. [PERSON] et al. [14] recently presented a comprehensive review of the role of AI in rotor fault diagnosis.