| 林俊亭,王 帅.基于 DBN-MPA-LSSVM 的无绝缘轨道电路故障诊断研究[J].电子测量与仪器学报,2022,36(9):37-44 |
| 基于 DBN-MPA-LSSVM 的无绝缘轨道电路故障诊断研究 |
| Research on fault diagnosis of jointless track circuitbased on DBN-MPA-LSSVM |
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| DOI： |
| 中文关键词: 无绝缘轨道电路 深度置信网络 海洋捕食者算法 最小二乘支持向量机 故障诊断 |
| 英文关键词:jointless track circuit deep belief network marine predators algorithm least squares support vector machine fault diagnosis |
| 基金项目:国家自然科学基金（52162050）、中国铁道科学研究院科研基金（2021YJ205）项目资助 |
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| 中文摘要: |
| 针对区间无绝缘轨道电路故障类型复杂、诊断精度低等问题,从故障特征提取和特征分类两方面出发,提出了一种深度
置信网络(DBN)和海洋捕食者算法(MPA)优化最小二乘支持向量机(LSSVM)的故障诊断方法。 首先,将集中监测数据和状态
标签输入到 DBN,以半监督的方式进行降维和特征提取,从而挖掘轨道电路不同故障特征信息;然后,采用 MPA 智能算法对
LSSVM 的惩罚因子和核函数参数进行寻优并建立最优 MPA-LSSVM 诊断模型;最后,将 DBN 提取的特征样本导入诊断模型进
行轨道电路的故障分类识别。 DBN-MPA-LSSVM 诊断模型充分利用了 DBN 在特征提取过程中的逐层提取优势以及 LSSVM 在
解决小样本情况下高维模式识别的优势。 实验验证与对比分析表明,DBN-MPA-LSSVM 模型测试集准确率为 98. 33%,MPA 优
化算法较 PSO、GWO、GA 算法模型诊断准确率分别提高了 6. 11%、3. 89%、3. 33%,平均准确率为 97. 98%,为基于数据驱动的轨
道电路故障诊断技术提供了一种新的方法。 |
| 英文摘要: |
| Aiming at the problems of complex fault types and low diagnosis accuracy of section jointless track circuit, a fault diagnosis
method of least squares support vector machine(LSSVM)optimized by deep belief network(DBN)and marine predators algorithm (MPA)
is proposed from the two aspects of fault feature extraction and feature classification. Firstly, the centralized monitoring data and status
labels are input into DBN, and the dimensionality reduction feature extraction is carried out in a semi supervised way, so as to mine the
different fault feature information of track circuit. Then, the intelligent algorithm MPA is used to optimize the penalty factor and kernel
function parameters of LSSVM, and the optimal MPA-LSSVM diagnosis model is established. Finally, the feature samples extracted by
DBN are introduced into the diagnosis model for fault classification and identification of track circuit. DBN-MPA-LSSVM diagnostic
model makes full use of the advantages of layer by layer extraction of DBN in the process of feature extraction and the advantages of
LSSVM in solving high-dimensional pattern recognition in the case of small samples. Experimental validation and comparative analysis
show that the DBN-MPA-LSSVM model test set accuracy is 98. 33%, and the MPA optimization algorithm improves the diagnosis
accuracy by 6. 11%, 3. 89%, and 3. 33% compared with PSO, GWO, and GA algorithm models, respectively, with an average
accuracy of 97. 98%, which provides a new data-driven rail circuit fault diagnosis technology based on method. |
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