This paper focuses on the study of uniform demagnetization fault diagnosis in permanent magnet synchronous motors （PMSMs） and proposes a novel method based on current signals for diagnosing faults at different degrees of uniform demagnetization. An improved ensemble empirical mode decomposition （EEMD） algorithm combined with particle swarm optimization-support vector machine （PSO-SVM） is introduced for fault diagnosis. First， the improved EEMD is employed to denoise and reconstruct the collected stator current signals. Then， the fractal box dimension of the processed data is calculated as the fault feature parameter. Finally， PSO-SVM is utilized to diagnose uniform demagnetization faults based on the extracted feature parameters. Simulation experiments and prototype testing demonstrate that the proposed method accurately identifies uniform demagnetization faults in PMSMs， achieving an average recognition rate of over 96%， thus validating the effectiveness of the proposed fault diagnosis approach.