Incipient faults in electrical drives can corrupt overall performance of high-speed trains; however, they are difficult to discover because of their slight fault symptoms. By sufficiently exploiting the distribution information of incipient faults, this paper presents the reason why incipient faults cannot be detected by the existing fault detection and diagnosis (FDD) methods. Under principal component analysis (PCA) framework, we propose a new data-driven FDD method, which is named probability-relevant PCA (PRPCA), for electrical drives in high-speed trains. The salient strengths of the PRPCA-based FDD method are: 1) it can greatly improve the fault detectability; it is suitable for non-Gaussian electrical drives; 2) based on the improved fault detectability, it can achieve accurate fault diagnosis via support vector machine; and 3) it can be easily applied to electrical drives even if neither physical models or parameters nor expert knowledge of drive systems is given; and it is of highly computational efficiency that can meet requirements on the real-time FDD. A set of experiments on a dSPACE platform-based traction system of the CRH2A-type high-speed train are carried out to demonstrate the effectiveness of the proposed method.

中文翻译：

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高速列车电力驱动早期故障的数据驱动检测与诊断

电力驱动的初期故障会破坏高速列车的整体性能；但由于故障症状轻微，不易被发现。通过充分利用早期故障的分布信息，本文提出了现有故障检测与诊断（FDD）方法无法检测早期故障的原因。在主成分分析 (PCA) 框架下，我们提出了一种新的数据驱动 FDD 方法，称为概率相关 PCA (PRPCA)，用于高速列车的电气驱动。基于 PRPCA 的 FDD 方法的显着优点是：1) 可以大大提高故障可检测性；适用于非高斯电驱动；2）基于改进的故障检测能力，可以通过支持向量机实现准确的故障诊断；3）即使没有物理模型或参数，也没有驱动系统的专业知识，它也可以很容易地应用于电气驱动；并且具有很高的计算效率，可以满足实时FDD的要求。在基于 dSPACE 平台的 CRH2A 型高速列车牵引系统上进行了一组实验，以证明所提出方法的有效性。