Reindexing Method for Ultralow-Sampling-Rate Data Used in the Diagnosis of Demagnetization Faults in IPMSM

This study proposes a data re-indexing method that integrates the Fast Fourier Transform (FFT) and a Genetic Algorithm (GA) to reconstruct features of back electromotive force (Back-EMF) from ultra-low sampling rate data (ULSD) obtained via a low-speed serial interface (e.g., RS-485) embedded in motor drives. Initially, a demagnetization model is developed to extract Back-EMF features. Subsequently, the ULSD is re-indexed based on varying numbers of sampling points per electrical period, and corresponding waveforms are reconstructed. FFT is applied to analyze rearranged waveforms, and two adjacent sampling configurations with the highest fundamental frequency amplitudes are identified. Finally, the GA optimizes the index assignment of the ULSD using these two candidate configurations, thereby enhancing the reconstruction of the Back-EMF waveform. Experimental results demonstrate that the proposed ULSD re-indexing method reconstructs Back-EMF waveforms. These waveforms are then used as inputs to a one-dimensional convolutional autoencoder (1-D CAE) combined with a multilayer perceptron (MLP) for demagnetization fault (DMF) diagnosis. The proposed approach achieves a high diagnostic accuracy of 96.7%, while reducing noise and sampling errors, and without incurring additional external signal capture interfaces (ESCIs) hardware costs. These findings validate the feasibility and effectiveness of applying rearranged ULSD for low-cost and reliable fault diagnosis in IPMSM systems.