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Fault Diagnosis and Safety Region Estimation based on SVM and EMD of Roller Bearing in Metro Vehicle

机译：基于SVM和Metro车辆滚子轴承SVM和EMD的故障诊断和安全区域估计

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Aiming at the problem of fault diagnosis and safety region boundary estimation of rolling bearing in metro vehicle, A method combined empirical mode decomposition (EMD) with support vector machine (SVM) was proposed. Firstly, The intrinsic mode functions (IMF) containing fault information was obtained by EMD decomposition based on the collected vibration data. Then, RMS value, energy, Shannon entropy and energy moments were calculated as characteristic index of roller bearing. Next the safety region boundary was demarcated by LSSVM and fault classification of bearings were identified by DAGSVM. The experiment results indicated that the proposed method could identify the working status and fault type of metro roller bearings accurately and effectively.
机译：针对地铁车辆中滚动轴承的故障诊断和安全区域边界估计的问题，提出了一种与支持向量机（SVM）的经验模式分解（EMD）的方法。首先，基于收集的振动数据，通过EMD分解获得包含故障信息的内在模式功能（IMF）。然后，RMS值，能量，香农熵和能量矩被计算为滚子轴承的特征指数。接下来，安全区域边界由LSSVM划分，并通过DAGSVM识别轴承的故障分类。实验结果表明，所提出的方法可以准确且有效地识别正常滚子轴承的工作状态和故障类型。

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