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Degradation state mining and identification for railway point machines

机译：铁路点机的退化状态挖掘与识别

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摘要

Critical point machine failure in railway-signal systems can lead to fatal accidents. Hence, early identification of anomalies is vital in guaranteeing reliable and safe transportation. However, most of the existing early fault diagnosis methods can only estimate the degradation trend under a specific fault mode. How to analyze the diversified degradation conditions under multiple fault modes is still a key problem. Considering the diversity of fault modes, this study proposes an early fault diagnosis method based on self-organizing feature map network and support vector machine, focusing on the use of non-fault data to simultaneously mine and accurately identify degradation states under different fault modes, to provide guidance for proactive machine maintenance. The experimental results obtained via application of this scheme to field data for railway point machines demonstrate that the proposed methodology can effectively mine and accurately identify degradation states with different machine characteristics.
机译：铁路信号系统中的临界点机器故障可能导致致命事故。因此，尽早发现异常对于保证可靠和安全的运输至关重要。但是，大多数现有的早期故障诊断方法只能估计特定故障模式下的退化趋势。如何分析多种故障模式下的多种退化条件仍然是关键问题。考虑到故障模式的多样性，本研究提出了一种基于自组织特征图网络和支持向量机的早期故障诊断方法，重点是利用非故障数据同时挖掘和准确识别不同故障模式下的退化状态，为主动维护机器提供指导。通过将该方案应用于铁路点机器的现场数据而获得的实验结果表明，该方法可以有效地挖掘和准确识别具有不同机器特征的退化状态。

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