The switch open-circuit fault signal of the Vienna rectifier possesses non-stationary characteristics and is also vulnerable to external interference factors, such as sensor noise and load variation. This phenomenon reduces the performance of traditional methods, including model-based and signal-based algorithms. In order to improve the accuracy, convergence rate, and robustness of diagnosis models, a hybrid deep learning Transformer–BiTCN optimized via ISGA (Improved Snow Geese Algorithm, ISGA) is proposed in this paper. Firstly, to assess the Vienna rectifier’s open-circuit fault signal, the time-varying and non-stationary characteristics generation mechanism is analyzed. Then, combining the fault signal characteristics of the Vienna rectifier, the hybrid deep learning model using Transformer–BiTCN, along with multi-scale feature fusion, is presented to extract hierarchical features, including both global temporal dependencies and local characteristics to enhance fault diagnosis accuracy and model robustness. Finally, the ISGA optimization algorithm with the Bloch initialization strategy and the Rime search mechanism is further presented to optimize the hyperparameters of the Transformer–BiTCN model so as to improve convergence and improve accuracy. Finally, the effectiveness of our proposed method is tested by simulations and experiments. It has been verified that the Transformer–BiTCN along with ISGA optimization is robust to non-stationary open-circuit fault signals and can achieve high diagnosis accuracy with a fast convergence rate.