## 1 Introduction  
With the recent rapid growth of the electric vehicle (EV) industry, integrated motor and drive (IMD) systems, which simultaneously provide high power density and efficiency, have attracted significant attention across various industrial sectors [1, 2]. IMD systems offer several advantages, including reduced installation space, lower wiring losses, suppression of electromagnetic interference, and improved overall system efficiency [3, 4]. Furthermore, an IMD system enables the elimination of components such as transformers and filters and offers the benefits of an optimized cooling structure and modular design, making it particularly well suited for EV applications that demand high power density [5, 6]. In line with this trend, extensive research has been conducted on compact, high-frequency IMD systems that employ PCB-based stators and GaN-based switching devices. Integrated designs incorporating GaN devices capable of operating at frequencies above 1 MHz have emerged as promising solutions for both reducing system size and suppressing current ripple [7, 8]. Electrical faults such as inter-turn faults (ITFs), bearing failures, and insulation breakdowns in motors--critical components in EV systems--can lead to system shutdowns, fires, and serious safety incidents during operation. As a result, diagnostic technologies,including fault-tolerant design approaches, have become increasingly important [9]. In particular, autonomous driving systems require immediate fault response and sustained control system stability due to the limited possibility of driver intervention [10; 11]. Recent studies have applied hybrid optimization algorithms for fault diagnosis and parameter estimation in power systems and electrical drives. These approaches offer improved robustness and accuracy in noisy or nonlinear environments [11; 12]. Motor faults that can occur during operation are diverse, and diagnostic approaches and countermeasures vary depending on the fault type. In general, motor faults are categorized into electrical, magnetic, and mechanical faults [13]. Electrical faults include inter-turn short circuits (ITSCs), phase-to-phase short circuits, and phase-to-ground faults [14; 15; 16; 17]. Magnetic faults consist of demagnetization and magnetic asymmetry [18; 19], while mechanical faults include bearing failures, rotor eccentricity, broken rotor bars, and air-gap asymmetry. Other possible faults include sensor failures and inverter faults [20; 21; 22]. Among these, ITSC faults are considered the most severe, as they can lead to total winding failure within a short period and generate significant torque ripple [23; 24]. This occurs because a localized short between turns produces extremely high circulating currents, resulting in severe overheating, insulation breakdown, and ultimately catastrophic motor failure or even fire [25; 26]. In particular, ITFs can rapidly destroy the entire winding and, in the case of permanent magnet (PM) motors, may even cause magnet demagnetization, posing a serious threat to overall system reliability [27; 28]. Various methods have been developed to diagnose ITFs due to their severity. These methods are considered to be among the most critical aspects of motor fault diagnosis [29; 30; 31; 32; 33; 28]. In [29; 30; 28], the widely adopted approach of motor current signature analysis (MCSA) is introduced. MCSA techniques are generally categorized into FFT-based spectrum analysis, time-frequency analysis (e.g., wavelet transform and Hilbert-Huang transform), and other methods. However, spectrum-analysis-based methods are highly sensitive to high-frequency noise and load variations, which can degrade diagnostic reliability. In [27], artificial intelligence (AI)-based diagnostic techniques, including neural networks and support vector machines (SVMs), have also been actively explored.