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An automatic abrupt information extraction method based on singular value decomposition and higher-order statistics

机译:基于奇异值分解和高阶统计量的突变信息自动提取方法

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摘要

One key aspect of local fault diagnosis is how to effectively extract abrupt features from the vibration signals. This paper proposes a method to automatically extract abrupt information based on singular value decomposition and higher-order statistics. In order to observe the distribution law of singular values, a numerical analysis to simulate the noise, periodic signal, abrupt signal and singular value distribution is conducted. Based on higher-order statistics and spectrum analysis, a method to automatically choose the upper and lower borders of the singular value interval reflecting the abrupt information is built. And the selected singular values derived from this method are used to reconstruct abrupt signals. It is proven that the method is able to obtain accurate results by processing the rub-impact fault signal measured from the experiments. The analytical and experimental results indicate that the proposed method is feasible for automatically extracting abrupt information caused by faults like the rotor-stator rub-impact.
机译:局部故障诊断的一个关键方面是如何有效地从振动信号中提取突变特征。提出了一种基于奇异值分解和高阶统计量自动提取突发信息的方法。为了观察奇异值的分布规律,进行了数值分析,模拟了噪声,周期信号,突变信号和奇异值的分布。基于高阶统计量和频谱分析,建立了一种自动选择反映突变信息的奇异值区间的上下边界的方法。并且从该方法导出的所选奇异值用于重建突变信号。事实证明,该方法能够通过处理从实验中测得的摩擦影响故障信号来获得准确的结果。分析和实验结果表明,所提出的方法是可行的,可以自动提取由转子-定子碰摩等故障引起的突变信息。

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