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首页> 外文期刊>Advances in Mechanical Engineering >Two-stage turnout fault diagnosis based on similarity function and fuzzy c-means:
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Two-stage turnout fault diagnosis based on similarity function and fuzzy c-means:

机译：基于相似度函数和模糊C-均值的两级道岔故障诊断：

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Fault diagnosis for turnouts is crucial to the safety of railways. Existing studies on fault diagnosis depend on human experiences to select reference curves and require fault type information beforehand. Therefore, we proposed a turnout fault diagnosis method, named similarity function and fuzzy c-means based two-stage algorithm to detect faults and identify fault types in real time. First, the reference curve is selected from current curves representing turnout actions by K-means algorithm; then, a similarity function called Fréchet distance is used to distinguish normal and abnormal curves. Second, an improved fuzzy c-means algorithm is employed to cluster curves automatically. To be more specific, it can double-confirm the normal curves recognized in the first step as well as divide the abnormal curves into different types. Furthermore, possible causes for each fault type are inferred according to their curves. Our approach integrates fault detection and fault classification into one model and would b.
机译：道岔的故障诊断对铁路安全至关重要。现有的故障诊断研究取决于人类的经验，以选择参考曲线并事先要求故障类型信息。因此，我们提出了一种道岔故障诊断方法，称为相似度函数和基于模糊c-均值的两阶段算法，用于实时检测故障和识别故障类型。首先，通过K-means算法从代表道岔动作的电流曲线中选择参考曲线;然后，使用一个称为Fréchet距离的相似度函数来区分正常曲线和异常曲线。其次，采用改进的模糊c均值算法对曲线进行自动聚类。更具体地说，它可以对第一步中识别的正常曲线进行双重确认，也可以将异常曲线分为不同类型。此外，根据它们的曲线推断出每种故障类型的可能原因。我们的方法将故障检测和故障分类集成到一个模型中，并且将b。

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