Scholarlink

<p>Inverter overheating is a critical fault factor in rail transit systems. To address the challenges of sparse low-voltage data and high-dimensional input features, we propose a hybrid prediction framework for inverter temperature. The Random Masked Dual DCGAN (RTDG) model is introduced to enhance low-voltage data diversity, while a Gaussian Markov Random Field (GMRF) method performs dimensionality reduction by identifying key variables. To capture spatio-temporal dependencies, an enhanced Transformer architecture (STTr) is constructed, integrating state space modeling and temporal normalization. These components are fused using a weighted stacking strategy. The model is trained and validated on real-world rail transit datasets. Performance is evaluated using MSE, RMSE, and MAE metrics. Experimental results show that the proposed model outperforms conventional approaches, achieving a 4.93% improvement over single models and a 9.73% gain compared to non-augmented training. This framework supports intelligent fault prevention and contributes to the safe, efficient operation of modern rail systems.</p>

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Research on temperature prediction method for rail transit train inverters based on spatial and timing improving Transformer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Yanhong Feng,
  • Yinan Zhai,
  • Fu Wang,
  • Xunran Yu,
  • Jinglei Li,
  • Xinyu Chen

摘要

Inverter overheating is a critical fault factor in rail transit systems. To address the challenges of sparse low-voltage data and high-dimensional input features, we propose a hybrid prediction framework for inverter temperature. The Random Masked Dual DCGAN (RTDG) model is introduced to enhance low-voltage data diversity, while a Gaussian Markov Random Field (GMRF) method performs dimensionality reduction by identifying key variables. To capture spatio-temporal dependencies, an enhanced Transformer architecture (STTr) is constructed, integrating state space modeling and temporal normalization. These components are fused using a weighted stacking strategy. The model is trained and validated on real-world rail transit datasets. Performance is evaluated using MSE, RMSE, and MAE metrics. Experimental results show that the proposed model outperforms conventional approaches, achieving a 4.93% improvement over single models and a 9.73% gain compared to non-augmented training. This framework supports intelligent fault prevention and contributes to the safe, efficient operation of modern rail systems.