Embedded-Oriented Open-Switch Fault Diagnosis and Localization for Dual Y-Connected PMSM Based on Deep Learning

The open-switch fault diagnosis speed of multiphase machines is generally no less than1/4 of an electrical cycle, severely affecting the timeliness of fault-tolerant control intervention. To address this issue, this paper first proposes a cascaded triggering algorithm based on logical judgment, integrating optimized open-phase characteristic values with harmonic subspace current trajectories to enhance the stability of the logic-based method. The proposed data-driven method treats open-phase characteristic values and harmonic subspace currents as input features to reduce the computational burden of extracting open-circuit features in deep learning networks. In addition, to improve the computational speed of the neural network on embedded platforms, a multitarget classification network is designed based on a simplified gated unit, and a lightweight solving algorithm for the activation function is applied. The dataset used in the deep learning network is labeled with the assistance of a designed autoencoder while preserving the transition data betweenpre-andpostfaultstates.Theeffectiveness, accuracy, and fault localization speed of the proposed algorithms are validated through the dataset and the experiment on the motor bench. The results demonstrate that the proposed data-driven method can accurately locate the open-switch fault within ten sampling periods.