An Unsupervised Fault-Detection Method for Railway Turnouts
An Unsupervised Fault-Detection Method for Railway Turnouts
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DOI：
10.1109/tim.2020.2998863
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发表时间：
2020-11-01
影响因子：
5.6
通讯作者：
Ye, Hao
中科院分区：
文献类型：
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作者：
Guo, Zijian;Wan, Yiming;Ye, Hao
铁路道岔需要高性能的状态监测，以防止灾难性的铁路事故。在工业实践中，道岔的监测通常由铁路工人进行，他们目视检查运行电流曲线。这导致了巨大的劳动力成本，并且容易出现人为错误。因此，通过故障检测算法实现道岔监测过程的自动化势在必行。现有的道岔现场数据给故障检测带来了三个困难：1）大量数据没有任何标签; 2）正常状态下采集的数据存在多个未知模式; 3）某些模式下样本数量较少。为了解决这些困难，本文提出了一种新的无监督故障检测方法，通过使用深度自编码器，该方法由未知模式的挖掘阶段和多模式故障检测阶段组成。首先，通过聚类和雇用工程师的专业知识来识别未知模式。然后，集成监测模型，包括本地监测模型开发的个别无故障模式和一个全球监测模型，通过合并的数据在所有的无故障模式，提出了提高整体故障检测性能。此外，为了在小样本情况下对模态进行局部建模，提出了一种单类迁移学习算法。在在线监测中，新到达的样本的决策利用局部模型和全局模型。利用模拟道岔数据和中国某高速铁路现场实测数据，通过与其他方法的比较，验证了该方法的有效性和鲁棒性。
Railway turnouts require high-performance condition monitoring to prevent disastrous railway accidents. In industrial practice, turnouts' monitoring is usually done by railway workers who visually inspect the operating current curves. This results in huge labor costs and prone to human mistakes. Thus, automating the process of turnouts' monitoring via fault-detection algorithms is imperative. The available turnout field data bring three difficulties to fault detection: 1) large amounts of data do not have any labels; 2) data collected in normal condition have multiple unknown modes; and 3) there are only a small number of samples in some modes. To address these difficulties, this article develops a novel unsupervised fault-detection method by using deep autoencoders, which is composed of an unknown modes' mining stage and a multimode fault-detection stage. First, unknown modes are identified through clustering and employing engineer expertise. Then, an ensemble monitoring model, consisting of local monitoring models developed with individual fault-free modes and a global monitoring model developed by merging the data in all fault-free modes, is proposed to improve the overall fault-detection performance. In addition, to construct local models for the modes with a small number of samples, a one-class transfer learning algorithm is presented. In online monitoring, the decision of a newly arrived sample exploits both local models and the global model. Using both the simulated turnout data and the field data collected from a high-speed railway in China, the efficacy and robustness of the proposed approach are demonstrated by comparisons with other methods.