| 彭菲桐,徐凯,吴仕勋,黄德青.基于智能优化深度网络的轨道电路故障诊断研究[J].电子测量与仪器学报,2024,38(2):219-230 |
| 基于智能优化深度网络的轨道电路故障诊断研究 |
| Research on fault diagnosis of track circuit based onintelligent optimization deep network |
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| DOI： |
| 中文关键词: 轨道电路故障诊断 卷积神经网络 长短期记忆网络 遗传算法 Q-learning |
| 英文关键词:fault diagnosis jointless track circuit convolutional neural networks LSTM network genetic algorithm Q-learning |
| 基金项目:重庆市自然科学基金(cstc2021jcyj msxmX0017)、重庆市教委科学技术研究项目(KJQN202000703)资助 |
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| Author | Institution |
| Peng Feitong | School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China |
| Xu Kai | School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China |
| Wu Shixun | School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China |
| Huang Deqing | School of Electrical Engineering,Southwest Jiaotong University, Chengdu 610031,China |
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| 中文摘要: |
| 针对无绝缘轨道电路故障的随机性和复杂性，采用单一诊断模型存在提取特征片面，且模型结构经验设计不合理的问题，提出一种智能优化深度网络的故障诊断方法。首先以轨道电路信号集中监测系统的6个电压检测量建立故障特征集，使用卷积神经网络（CNN）提取特征空间信息，长短期记忆网络（LSTM）提取时间特征信息，从而让轨道电路故障诊断所提取的特征兼具时空信息；同时，引入遗传算法（GA）优化上述深度神经网络的结构及参数，并结合强化学习中的Q-learning方法对两个组合网络特征级的输出权重进一步优化；最后，使用多层感知器（MLP）对深度网络的分类误差进行拟合修正，提高模型对轨道电路的故障诊断精度。仿真结果表明，利用智能优化的深度网络模型对轨道电路的故障诊断相较于单一模型、精炼设计的组合模型识别率可达99.28%，评价指标等均有所提升，具有更高的故障诊断准确度，证明了智能优化深度网络能进一步提高轨道电路的故障诊断性能。 |
| 英文摘要: |
| Aiming at the randomness and complexity of jointless track circuit faults, the single diagnosis model has the problems of one-sided extraction features and unreasonable empirical design of model structure. A fault diagnosis method based on intelligent optimization deep network is proposed. Firstly, the fault feature set is established by six voltage detection quantities of the track circuit signal centralized monitoring system. The convolutional neural network (CNN) is used to extract the feature space information, and the long short-term memory network (LSTM) is used to extract the time feature information, so that the features extracted by track circuit fault diagnosis have both spatial and temporal information. At the same time, the genetic algorithm (GA) is introduced to optimize the structure and parameters of the aforementioned deep neural network, and the output weight of the feature level of the two combined networks is further optimized by combining the Q-learning method in reinforcement learning. Finally, the multi-layer perceptron (MLP) is used to fit and correct the classification error of the deep network to improve the fault diagnosis accuracy of the model for the track circuit. The simulation results show that the recognition rate of the fault diagnosis of the track circuit using the intelligent optimized deep network model can reach 99.28% compared with the single model and the refined design combination model. The evaluation index is improved, and the fault diagnosis accuracy is higher. It is proved that the intelligent optimized deep network can further improve the fault diagnosis performance of the track circuit. |
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