| Citation: | CAO Jingqi, WANG Yankai, NIE Minghong, et al. Decision-level information fusion-based motor fault diagnosis for electric propulsion systems[J]. Journal of Aerospace Power, 2025, 40（12）：20240860 doi: 10.13224/j.cnki.jasp.20240860
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The permanent magnet synchronous motor is a critical component of electric propulsion systems, and its operational status is integral to the system’s safe functioning. Failure of the permanent magnet synchronous motor (PMSM) can trigger multiple physical quantity changes, making it challenging to achieve accurate fault diagnosis relying on a single signal source. To address this issue, a decision-level multi-channel information fusion fault diagnosis method was proposed, by combining convolutional neural networks and gated recurrent units (CNN-GRU) and improved Dempster-Shafer (D-S) evidence argumentation. Initially, analytical and finite element methods were employed to quantitatively analyze the vibration and current frequency domain characteristics of local demagnetization and rotor eccentricity faults in permanent magnet synchronous motors, thereby enhancing the interpretability of diagnostic results. Subsequently, a decision-level fusion diagnostic model was established, by integrating CNN-GRU and improved D-S evidence theory based on Pignistic probability distance and weighted Deng entropy. Finally, a motor fault simulation tester was constructed, and the model was validated using experimental data. The results demonstrated that multi-channel fault diagnosis is superior to single-channel diagnosis results. The decision-level fault diagnosis based on multi-source data, with the fusion of 4 channels, achieved diagnostic accuracy of 100%, 100%, and 99.3%, respectively, under three operating conditions. The proposed method accurately identified the types of permanent magnet synchronous motor faults, providing a reference for fault diagnosis in electric propulsion systems and offering potential value for engineering applications.
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