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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：铁路信号系统中的关键点机器故障可能导致致命事故。因此，早期识别异常对保证可靠和安全的运输至关重要。然而，大多数现有的早期故障诊断方法只能在特定故障模式下估算降低趋势。如何分析多种故障模式下的多元化降级条件仍然是一个关键问题。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映射网络和支持向量机的早期故障诊断方法，专注于使用非故障数据来同时挖掘和准确地识别不同故障模式下的降级状态，为主动机器维护提供指导。通过应用该方案获得的实验结果对铁路点机器的现场数据证明了所提出的方法可以有效地挖掘和准确地识别具有不同机器特性的劣化状态。

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