Mechanical Fault Diagnosis Method of Disconnector Based on Parallel Dual-Channel Model of Feature Fusion

Mechanical fault samples of disconnectors are scarce, the fault types vary, and the self-evidence is weak, which leads to a lack of perfect fault diagnosis methods, and hidden defects cannot be found in time. To solve this problem, a mechanical fault diagnosis method for disconnectors based on a parallel dual-channel feature fusion model is proposed. Firstly, the optimal parameters for variational mode decomposition (VMD) are obtained using the black-winged kite algorithm (BKA). After the signal decomposition, the kurtosis values of each intrinsic mode function (IMF) are calculated, screened, and reconstructed. The reconstructed signal is input into the gated recurrent unit (GRU) to capture its time-series characteristics. Then, the vibration signal is generated by the recurrence plot (RP) to generate the atlas set and input into the vision Transformer (ViT) to extract its spatial characteristics. Finally, the time-series and spatial characteristics are fused, the multi-head self-attention mechanism is used for training, and softmax is used for fault classification. The measured data results show that the diagnostic accuracy of the model for mechanical fault types reaches 97.9%, which is 3.2%, 4.3%, 1.0%, 2.4%, 2.9%, 1.8%, 2.1%, 9%, and 7.5% higher than the other nine models numbered #2-#10, respectively, verifying its effectiveness and adaptability.