| 李斌,舒嘉辉,严灵潇,田浩.改进黑翅鸢算法的1D-2D-GAF-PCNN-GRU-MSA弓网电弧检测应用[J].电子测量与仪器学报,2024,38(10):201-211 |
| 改进黑翅鸢算法的1D-2D-GAF-PCNN-GRU-MSA弓网电弧检测应用 |
| 1D-2D-GAF-PCNN-GRU-MSA pantograph arc detection applicationbased on improved black-winged kite algorithm |
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| DOI： |
| 中文关键词: 高速气流场 改进黑翅鸢算法 特征融合 格拉姆角场 故障检测 |
| 英文关键词:high-velocity airflow field improved black-winged kite algorithm feature fusion gram-angle field fault diagnosis |
| 基金项目:国家自然科学基金(51674136)、2024年辽宁省教育厅基本科研项目（LJ232410147055）资助 |
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| Author | Institution |
| Li Bin | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Shu Jiahui | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Yan Lingxiao | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Tian Hao | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
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| 中文摘要: |
| 针对高速列车运行时高速气流场对受电弓碳滑板与接触网之间的接触压力和电弧状态影响。通过计算得出更符合实际状态下的接触压力和电弧状态模型，建立了考虑高速气流场影响的弓网电弧实验模型。提出了改进黑翅鸢算法（IBKA）的1D-2D-GAF-PCNN-GRU-MSA故障检测模型。应用格拉姆角场（GAF）将一维接触电压信号时序图象化转换为二维图像并通过双通道卷积神经网络（PCNN）进行特征识别。另将一维时序信号通过门控循环单元（GRU）捕捉时序信号特征。将一维时序信号特征与二维图像特征进行特征融合，弥补各自局限性。针对模型中的难以确定的学习率、门控循环单元网络层神经元个数等参数，融入改进黑翅鸢算法（IBKA）对参数寻优使模型更加合理。最后，融合多头自注意力机制提高模型准确率。将提出的模型与其他3种模型分别对3组不同实验条件的弓网电弧模型进行检测，验证提出的模型具有较强的鲁棒性和较高的准确性。 |
| 英文摘要: |
| The influence of high-speed airflow field on the contact pressure and arc state between the pantograph carbon slide plate and the catenary during the operation of high-speed train was analyzed. By calculating the contact pressure and arc state models that are more in line with the actual state, an experimental model of pantograph arc considering the influence of high-speed airflow field is established. In this paper, a 1D-2D-GAF-PCNN-GRU-MSA fault detection model based on the improved black-winged kite algorithm （IBKA）was proposed. The gram-angle field (GAF) was used to convert the one-dimensional contact voltage signal into a two-dimensional image, and the feature recognition was carried out by the parallelizing convolutional neural network (PCNN). In addition, the one-dimensional timing signal is captured by the gated recurrent unit (GRU). The features of the one-dimensional time-series signal and the two-dimensional image are fused to make up for their respective limitations. In view of the parameters in the model, such as the learning rate that is difficult to determine, the number of neurons in the network layer of the gated recurrent unit, and the improved black-winged kite algorithm is integrated to optimize the parameters to make the model more reasonable. Finally, the multi-head self-attention mechanism was fused to improve the accuracy of the model. The proposed model and other three models were tested on three sets of pantograph-net arc models with different experimental conditions, and it was verified that the proposed model had strong robustness and high accuracy. |
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