Circular domain features based condition monitoring for low speed slewing bearing

摘要: This paper presents a novel application of circular domain features calculation based condition monitoring method for low rotational speed slewing bearing. The employs data reduction process using piecewise aggregate approximation (PAA) to detect frequency alteration in the bearing signal when fault occurs. From processed data, such as mean, variance, skewness and kurtosis are calculated monitored. It is shown that slight changes during operation can be identified more clearly analysis compared time other advanced processing methods wavelet decomposition empirical mode (EMD) allowing engineer better schedule maintenance work. Four were consistently identify onset (initiation) from peak feature value which not observable features. demonstrated with simulated laboratory industrial Coal Bridge Reclaimer used local steel mill.