Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks
Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks
复制标题
基于深度神经网络的高速列车大数据智能故障诊断
DOI：
10.1109/tii.2017.2683528
复制
发表时间：
2017-08-01
影响因子：
12.3
通讯作者：
Wang, Huihui
中科院分区：
文献类型：
--
作者：
Hu, Hexuan;Tang, Bo;Wang, Huihui
转向架是高速列车的重要组成部分。转向架的机械性能水平对高速列车的安全性和可靠性有着重要的影响。因此，利用大数据对转向架进行故障诊断非常重要。转向架故障机理复杂，特征信号不明显。由于这些原因，传统的信号处理方法不能有效地提取转向架的故障信息。因此，本文采用深度神经网络对转向架进行故障识别。深度神经网络在这方面提供了许多好处。利用深度神经网络，可以自适应地提取信号频谱中的故障信息。该技术不依赖于广泛的信号处理知识和诊断经验。与传统的智能诊断方法相比，深度神经网络可以获得更高的诊断准确率。此外，深度神经网络不依赖于样本量，即使在样本量相对较小的情况下，也可以获得较高的诊断准确率。该方法应用于不同速度、不同故障的高速列车，均取得了很高的诊断准确率，具有广泛的适用性。此外，深度神经网络在正常情况下的识别准确率可以达到100%。该方法为高速列车大数据故障诊断提供了一种新的范式，在该领域具有重要的应用价值。
Bogies are an important component of high-speed trains. The level of mechanical performance of bogies has a major influence on the safety and reliability of high-speed train. Therefore, conducting fault diagnoses on bogies with big data is very important. Fault mechanisms of bogies are very complex, and feature signals are nonobvious. For these reasons, fault information of bogies cannot be effectively extracted using the traditional signal processing method. Therefore, this paper adopted the deep neural network to recognize faults in bogies. The deep neural network offers numerous benefits in this context. Using deep neural networks, fault information in a signal spectrum can be extracted in a selfadaptive method. This technique is free of dependence on extensive signal processing knowledge and diagnostic experience. Compared with the traditional intelligent diagnosis method, the deep neural network can obtain a higher diagnostic accuracy. Additionally, the deep neural network does not depend on the sample size, and it can obtain high diagnostic accuracy even when the sample size is relatively small. It also achieves very high diagnostic accuracy applied to high-speed trains with different speeds and different faults, which shows that the method is extensively applicable. Furthermore, the recognition accuracy rate of the deep neural network under normal conditions can reach 100%. This method provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.