[Objective] Power electronic components in DC motor systems are prone to soft faults under prolonged high-frequency switching operation. To address the problems in soft fault diagnosis, including insufficient fusion of time-frequency domain features and low recognition accuracy, this paper proposes a fault diagnosis method based on the LSTM-CAM-Transformer model by combining the long short-term memory (LSTM) network and the multi-scale time-frequency domain cross-attention mechanism (CAM). [Methods] Firstly, the collected fault signal was preprocessed, and the parameters of the variational mode decomposition (VMD) algorithm were optimized using the Rime optimization algorithm (RIME), which accurately obtained the optimal combination of the decomposition modals K and the penalty factor α, and effectively removed the noise and interference components in the signal. Then, the 5-dimensional time domain parameters and 5-dimensional frequency domain parameters of each intrinsic modal function were extracted, which were used as the feature vectors for fault diagnosis. Finally, a multi-scale time-frequency domain CAM was utilized to strengthen the information interaction between the time domain and frequency domain of the feature vectors, allowing the model to further mine the time-frequency domain features of the signals. [Results] The model proposed in this paper was compared with the other four models to verify its superiority. The experimental results showed that compared with the other four models, the LSTM-CAM-Transformer model proposed in this paper has the fastest convergence speed, better stability and generalization, and outperforms the other four models in diagnostic accuracy, F1 score, loss value and recall. [Conclusion] The LSTM-CAM-Transformer model proposed in this paper effectively solves the problem of insufficient fusion of time-frequency domain features in the traditional method by integrating the signal preprocessing strategy of RIME-based improved VMD signal preprocessing strategy with the CAM time-frequency feature enhancement mechanism, which provides an efficient and reliable new method for the soft fault diagnosis of the power electronic equipment in the DC motor system.