## 7 Conclusions and Discussion  
In this study, a simulation-based approach is employed to diagnose ITSCF in IPMSM using the DWT analysis of motor voltage. Initially, an error model for the internal phase-to-phase short circuit (ITSC) in the IPMSM windings was derived. The characteristic errors generated during inter-turn short circuits were calculated, and the ITSC simulation was performed using the integration of MATLAB/Simulink/Simscape. Subsequently, the ITSC simulation results were analyzed to understand the relationships among the relevant characteristics. Finally, the motor voltage under ITSC conditions was analyzed using wavelet transform to verify the proposed concept and methodology. The results demonstrate that specific frequencies corresponding to the characteristic features of internal short-circuit faults can be extracted from stator voltage using discrete wavelet transform when a short circuit occurs within the stator winding. The simulation results validate the proposed method, indicating that wavelet transform offers a relatively accurate detection technique. This study primarily focuses on data analysis during steady-state motor operation. Although DWT's time-frequency analysis is well-suited for variable speed conditions, more sophisticated diagnostic tools, including CNNs and AEs, are required to extract meaningful information from the DWT data in these scenarios.  
Furthermore, the proposed IPMSM ITSCF feature extraction system framework significantly contributes to the field of IPMSM diagnosis by improving the performance of condition-based monitoring and DWT-based techniques. In industrial applications, after capturing information such as voltage, current, and speed from the IPMSM on the production line, it can be transmitted to the upper-level controller, such as a web-based cloud server, via industrial buses such as EtherCAT, CANBus, and others. The wavelet transform method can be easily implemented on these devices, utilizing their computational resources to perform wavelet transform calculations, thereby enabling the diagnosis of ITSCF.  
Moreover, the system offers valuable insights for dataset collection in the development of deep learning models aimed at identifying various motor faults. The findings of this study are expected to have a high degree of applicability, potentially extending to the  
Figure 28: Energy percentage with healthy condition and ITSCF at 600 rpm, 25% load.  
Figure 29: The FFT results for \(V_{q}\) at 600 rpm, 25% load: (**a**) healthy condition, (**b**) ITSCF.  
diagnosis of other types of electrical machine faults. This method could also be applied to fault diagnosis in Surface Permanent Magnet Synchronous Motors (SPMSMs), Synchronous Reluctance Motors (SynRMs), and other similar systems. In the future, the research results of the IPMSM ITSCF feature extraction system framework could be used to address the extensive data preprocessing requirements for designing deep learning models and be applied to the field of AI-based health monitoring of industrial equipment.  
Conceptualization, F-C.L. and C.-S.C.; methodology, F-C.L. and C.-S.C.; software, F.-J.Y.; validation, F-J.Y.; formal analysis, C.-S.C.; investigation, C.-J.L. and F.-C.L.; resources, C.-S.C.; data curation, F.-J.Y.; writing--original draft preparation, F.-C.L. and C.-J.L.; writing--review and editing, C.-J.L. and F.-C.L.; visualization, C.-J.L.; supervision, F.-C.L.; project administration, F.-C.L.; funding acquisition, C.-S.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. This work is supported by the National Science and Technology Council, NSTC 113-2222-E-027-004, and the Industrial Technology Research Institute.  
The authors declare no conflicts of interest.