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Circular domain features based condition monitoring for low speed slewing bearing

机译:基于圆域特征的低速回转支承状态监测

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摘要

This paper presents a novel application of circular domain features calculation based condition monitoring method for low rotational speed slewing bearing. The method employs data reduction process using piecewise aggregate approximation (PAA) to detect frequency alteration in the bearing signal when the fault occurs. From the processed data, circular domain features such as circular mean, circular variance, circular skewness and circular kurtosis are calculated and monitored. It is shown that the slight changes of bearing condition during operation can be identified more clearly in circular domain analysis compared to time domain analysis and other advanced signal processing methods such as wavelet decomposition and empirical mode decomposition (EMD) allowing the engineer to better schedule the maintenance work. Four circular domain features were shown to consistently and clearly identify the onset (initiation) of fault from the peak feature value which is not clearly observable in time domain features. The application of the method is demonstrated with simulated data, laboratory slewing bearing data and industrial bearing data from Coal Bridge Reclaimer used in a local steel mill.
机译:本文提出了一种基于圆域特征计算的状态监测方法在低转速回转支承中的新应用。该方法采用数据缩减过程,该过程使用分段聚合近似(PAA)来检测故障发生时轴承信号中的频率变化。根据处理后的数据,可以计算和监视圆域特征,例如圆均值,圆方差,圆偏度和圆峰度。结果表明,与时域分析和其他高级信号处理方法(如小波分解和经验模式分解(EMD))相比,在圆形域分析中可以更清楚地识别出运行过程中轴承状况的细微变化。维护工作。图中显示了四个圆形域特征,可以从峰值特征值一致而清晰地识别出故障的发生(开始),而峰值在时域特征中是无法观察到的。通过在本地钢厂使用的煤桥取料机的模拟数据,实验室回转支承数据和工业支承数据证明了该方法的应用。

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