Multifunction vehicle bus (MVB) is the most widely used train communication network which transmits controlling and supervising data. The faults of MVB will heavily affect the train's safe and stable operation. Due to the harsh operating environment and distributed structure, the MVB fault diagnosis has always been a difficult issue in the maintenance of the train. Many MVB faults will distort the physical waveforms and cause serious packet loss. Thus, we have extracted waveform features to characterize different MVB faults and turned the fault diagnosis into a pattern recognition problem. Then a classifier based on weighted support vector machine (WSVM) has been trained to diagnose and locate network faults. Considering that samples locating in different positions of the feature space have different influences on the support vector machine (SVM) hyperplane, we have proposed a multi‐hop edge approaching method to assign sample weights in WSVM. To identify the position of the tested sample, the hops from its location to the classification margin have been counted. The less the hop‐count, the closer to the classification margin and the larger the sample weight. Compared with SVM and other WSVM methods, the proposed method has better performance on the artificial synthetic datasets, the MVB dataset, and the benchmark datasets. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

中文翻译：

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基于加权支持向量机的列车通信网络故障诊断

多功能车辆总线（MVB）是使用最广泛的火车通信网络，可传输控制和监督数据。MVB的故障将严重影响列车的安全稳定运行。由于恶劣的运行环境和分布式结构，MVB故障诊断一直是列车维护中的难题。许多MVB故障将使物理波形失真并导致严重的数据包丢失。因此，我们提取了波形特征以表征不同的MVB故障，并将故障诊断转变为模式识别问题。然后训练了基于加权支持向量机（WSVM）的分类器，以诊断和定位网络故障。考虑到位于特征空间不同位置的样本对支持向量机（SVM）超平面的影响不同，我们提出了一种多跳边逼近方法来分配WSVM中的样本权重。为了确定测试样品的位置，已经计算了从其位置到分类余量的跃点。跳数越少，越接近分类裕度，样本权重越大。与SVM和其他WSVM方法相比，该方法在人工合成数据集，MVB数据集和基准数据集上具有更好的性能。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布 为了确定测试样品的位置，已经计算了从其位置到分类余量的跃点。跳数越少，越接近分类裕度，样本权重越大。与SVM和其他WSVM方法相比，该方法在人工合成数据集，MVB数据集和基准数据集上具有更好的性能。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布 为了确定测试样品的位置，已经计算了从其位置到分类余量的跃点。跳数越少，越接近分类裕度，样本权重越大。与SVM和其他WSVM方法相比，该方法在人工合成数据集，MVB数据集和基准数据集上具有更好的性能。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布 ©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布 ©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布