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Intrinsic mode function determination of faulty rolling element bearing based on kurtosis

机译:基于峰度的滚动轴承故障本征函数确定

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Feature extraction of faulty rolling element bearings (REBs) is important in condition monitoring and faults diagnosis of rotating machinery. The empirical mode decomposition (EMD) is a useful tool for non-stationary signal analysis, by which the analyzed vibration generated by the faulty REBs can be decomposed into a set of intrinsic mode functions (IMFs). However, how to determine an interesting intrinsic mode function (IMF) is often difficult in the applications of EMD. To address this issue, a kurtosis based scheme for the feature extraction of faulty REBs has been introduced in this paper. In the scheme, the vibration generated by REBs is acquired firstly. Then, the EMD is performed to decompose the vibration into a set of IMFs. Lastly, the kurtosis is calculated to determine the interesting IMF, which has the maximum kurtosis value. Simulation and test results supported the introduced scheme positively.
机译:故障滚动轴承(REBs)的特征提取对于旋转机械的状态监测和故障诊断很重要。经验模式分解(EMD)是用于非平稳信号分析的有用工具,通过该工具,可以将故障REB产生的已分析振动分解为一组固有模式函数(IMF)。然而,在EMD的应用中,通常难以确定有趣的固有模式函数(IMF)。为了解决这个问题,本文介绍了一种基于峰度的故障REB特征提取方案。在该方案中,首先获取由REB产生的振动。然后,执行EMD以将振动分解为一组IMF。最后,计算峰度以确定有趣的IMF,该IMF具有最大峰度值。仿真和测试结果积极地支持了引入的方案。

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