The performance of the proposed method was evaluated through extensive simulations and experimental validations under various load conditions. The experimental results confirmed that the method can detect faults with a high accuracy of approximately 98%. This method uses advanced signal processing techniques such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) to analyze the frequency components of motor signals, enabling precise identification of fault characteristics.

The research emphasizes the practical implementation of the algorithm in real motor control environments, addressing issues related to operational and measurement noise. By integrating this fault diagnosis strategy into existing motor control systems, it is possible to reduce costs and complexity while maintaining robust performance. The study discusses the potential for using the algorithm with low-cost microprocessors and suggests future research directions to enhance the fault detection capabilities of PMSMs in critical applications.