However, the choice of an appropriate window size, tailored to specific frequency components of the signal, is crucial. The fixed-length window of the STFT may not effectively capture varying frequency components, limiting its application to motors with very slow speed variations. The lack of sufficient and representative data poses a significant challenge in this research field, resulting in inefficient utilization of time and resources. Additionally, early warning signs are often subtle, making them difficult to capture effectively, and require prolonged testing under extreme operating conditions. The instability of analysis methods also complicates the establishment of deep learning models. Therefore, this paper proposes a deep learning model design pre-processing concept for ITSCF in IPMSMs and innovatively applies wavelet transform to IPMSM fault diagnosis. Firstly, a motor ITSCF model is established to identify the second harmonic component fault characteristics. A simulation program combining MATLAB/Simulink/Simscape is developed to generate phase voltage and short-circuit coil circulating current waveforms during ITSCs. Next, the discrete wavelet transform (DWT) method is employed to perform wavelet transformation on the \(q\)-axis synchronous voltage and extract the second harmonic component fault characteristics based on energy principles. When a stator coil experiences a short circuit, discrete wavelet analysis of the stator voltage is used to extract specific frequencies associated with the ITSCF characteristics. The aim of this study is to improve the detection accuracy of motor fault features and effectively identify different types and severities of ITSCF. It provides a deep learning model for IPMSM health diagnosis, offering a more precise preprocessing design. The simulation results demonstrate that the wavelet transform method exhibits high accuracy in fault detection. Based on these findings, this study provides the occurrence time of faults and their corresponding frequencies, accurately pinpointing the time of ITSC events and the resulting second harmonic component fault characteristics. This demonstrates the effectiveness of the proposed technique for detecting ITSCF in motors [8]. The contributions of this study include not only establishing a reference process for designing a deep learning feature model for motor ITSCs but also successfully applying wavelet transform to effectively analyze the time-frequency characteristics of the signals. Through the proposed method, motor fault features can be more accurately identified, thereby reducing the probability of incorrect diagnoses and improving detection efficiency. Moreover, this wavelet transform-based diagnostic technique effectively addresses non-stationary signals during motor operation, overcoming the limitations of traditional methods in terms of time and frequency resolution. The contributions of this paper are as follows:* Successfully established an IPMSM inter-turn short circuits fault feature extraction system framework, providing the data preprocessing required for designing deep learning models for motor failure analysis. * Derived the physical model of the IPMSM ITSCF and successfully built simulation equations using MATLAB/Simulink/Simscape. * Innovatively extracted ITSC fault features using wavelet transform, enabling the diagnosis of IPMSM ITSC failures. * The proposed ITSC time-frequency features and ITSC energy level information can not only be used for motor fault diagnosis but also for determining the range of command failures based on energy characteristics.