## 1 Introduction  
Drive systems play a crucial role in modern industries, such as steel mills, wind turbines, and industrial robots. Many of these systems can be represented as a model, where the motor and load inertia are connected by a shaft. In such applications, low mechanical resonance can occur due to the flexible shaft between the actuator and the load inertia. This can cause undesirable problems in the mechanical coupling of the system and decrease its performance [1; 2; 3; 4]. To address these issues, the dynamics of these flexible systems must be modeled as a two-mass or multi-mass system when designing the control laws. However, incorrect calculations and assumptions of the parameters in these models can be problematic when relying solely on the information provided in the manufacturer's datasheets [5; 6; 7]. Without knowledge of the inertia and shaft stiffness, it is difficult to effectively suppress mechanical resonance. Identifying these parameters can help suppress mechanical resonance, improve system response, and reduce tracking errors. Additionally, these mechanical parameters may not be constant over time in some practical applications. A mechanical system with inertia disks distributed on the actuating motor and its load side, connected by a flexible shaft between them, can be considered a 2 MM system. A typical 2 MM drive system exhibits resonant and anti-resonant frequencies when the system's frequency response is measured from the applied electromagnetic torque to the measured speeds of the actuating motor. Over the years, numerous methods and approaches have been developed and applied to extract the modal parameters of the system. The 2 MM model has been used to describe the mechanics of various systems, including rolling mills [8], wind turbines [9], industrial robots [10], paper machines [11], and elevators [12]. Furthermore, the 2 MM model has been employed in designing control laws to mitigate harmful resonances in mechanical systems and improve dynamic accuracy. Recent years have seen a rise in the application of artificial intelligence (AI), machine learning (ML), and deep learning (DL) within the engineering and industrial electronics sectors [13; 14; 15]. These methodologies are now commonly used for complex tasks such as system identification, control, optimization, and fault diagnostics. For example, in power electronics, AI is utilized in system design, control, and maintenance [13]. Deep learning, a subset of ML, is making strides in fault diagnostics in electrical applications, contributing to improved system reliability [14]. Additionally, deep learning methods, such as sparse autoencoders, have been successfully employed in improving the reconstruction of fingerprint images, further expanding the scope of AI applications [16]. In fact, AI and ML technologies hold promising prospects, but their wider adoption in industry and society faces several challenges from both internal and external perspectives [15]. However, solutions are being sought, and these techniques are being incorporated into traditional engineering methods, such as model-based multivariable control systems [17]. In drive systems, AI and ML offer advanced techniques for parameter identification and system modeling, with applications like the use of dynamic neural networks for estimating nonlinear friction [18]. The intersection of AI, ML, DL, and engineering indicates a new era in industrial electronics, characterized by increased efficiency, robustness, and performance. Parameter identification methods in the literature can be roughly divided into parametric and non-parametric methods. Parametric methods involve estimating the parameters of the 2 MM system's transfer function polynomials in the time domain. On the other hand, non-parametric methods utilize frequency response function (FRF) methods to estimate the resonant frequencies, i.e., the modal parameters of the system. Many approaches have been successfully applied for the parameter identification of 2 MM systems.