## 1 Introduction
However, AI-based approaches require large amounts of labeled fault data for effective training, and acquiring sufficient and diverse fault data under various operating conditions remains a major challenge in real-world industrial environments. Furthermore, conventional model-based methods require complex mathematical modeling and are susceptible to uncertainties in motor parameters [31; 32]. ITF diagnosis based on impedance analysis in induction motors (IMs) was introduced in a previous study [33]. The impedance characteristics of six winding connections were analyzed, and the phase of impedance was calculated to diagnose the ITF. The six winding connections are required to calculate the phase of impedance for ITF diagnosis. Although the introduced ITF diagnosis method is simplified compared to other methods, it presents practical limitations when applied to real industrial systems such as electric vehicles (EVs), which commonly utilize IMD systems. In these systems, the physical disconnection of the inverter from the motor is required to perform offline measurements, which significantly limits the applicability of the approach in IMD systems. This paper proposes an offline ITF diagnosis algorithm for IMD systems based on impedance analysis and the space voltage vector control of a three-phase inverter. The proposed method enables fault detection without mechanical separation between the motor and the inverter, making it highly suitable for IMD-based applications. Assuming an ITF occurs in phase A, the terminal impedances are analyzed and compared to identify the maximum and minimum impedance values. Although ITFs can be diagnosed through terminal resistance measurements, the terminal configuration in IMD systems differs due to the integrated structure of the motor and inverter. Therefore, the space voltage vector of the three-phase inverter is analyzed for each switching state. A fault diagnosis algorithm is proposed based on both the impedance characteristics and the space voltage vector analysis. To validate the proposed algorithm, an experimental setup was constructed using a 12 kW three-phase induction motor (IM), an inverter, fault resistance, and a terminal box for winding connections. The algorithm was verified for both Y and \(\Delta\) connections without requiring access to mechanical terminal connections. Here, we provide a summary of the main contributions of our work:  
1. To diagnose the ITF, the impedance of electrical machine is analyzed for the terminal connection. The terminal connection is implemented electrically through the space vector control of the three-phase inverter, eliminating the need for inverter disconnection or access to terminal connections when the ITF of the electrical machine is diagnosed. 2. The proposed method does not require the tracking of time-varying operational parameters, thereby simplifying implementation compared to online diagnostic approaches. Furthermore, a low-complexity algorithm based on phase impedance comparison across six space voltage vectors is employed, enhancing compatibility with embedded systems and real-time control platforms. 3. A 12 kW three-phase IM was manufactured, and an experimental environment was implemented to verify the proposed algorithm for ITF diagnosis. The proposed ITF diagnosis algorithm was verified through the measurement of impedance for the space voltage vector for Y and \(\Delta\) connections. This paper is organized as follows: Section 2 describes the analysis of the impedance characteristics under ITF conditions in phase A for both Y and \(\Delta\) connections. Section 3 presents the space voltage vector analysis corresponding to inverter switching states and proposes the ITF diagnosis algorithm based on impedance variation. Section 4 describes the experimental validation using the fabricated IM and terminal box. Section 5 concludes the paper.