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A Rolling Element Bearing Diagnosis Method Based on Singular Value Decomposition and Squared Envelope Spectrum

机译:基于奇异值分解和平方包络谱的滚动元件辅助诊断方法

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The vibration caused by an early defect on the rolling element bearing (REB) is very weak and easy to be submerged other signals and noise. Therefore, the performance of a bearing fault diagnosis method mainly depends on two key steps, namely, bearing fault signal component extraction and bearing fault type identification. In this article, authors have proposed a bearing fault diagnosis method that combines the techniques of squared envelop spectrum (SES) analysis and singular value decomposition (SVD). The original vibration signal will be decomposed into several sub-signals through SVD. Then, sub-signals are grouped according to their similarity. Later, the SES of the grouped signal is applied to identify the fault type. The performance of this method is tested through actual vibration signals obtained from the test-rig.
机译:由滚动元件轴承(REB)上的早期缺陷引起的振动非常弱,易于浸没其他信号和噪声。 因此,轴承故障诊断方法的性能主要取决于两个关键步骤,即轴承故障信号分量提取和轴承故障类型识别。 在本文中,作者提出了一种轴承故障诊断方法,该方法结合了平方包频谱(SES)分析和奇异值分解(SVD)的技术。 原始振动信号通过SVD分解成几个子信号。 然后,子信号根据其相似性进行分组。 稍后,应用分组信号的SES以识别故障类型。 通过从测试钻机获得的实际振动信号测试该方法的性能。

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