Fault detection method of gantry slewing bearing based on improved comprehensive features

As an important lifting equipment of bulk cargo terminal, the vibration signal of its slewing support has the characteristics of low speed, heavy load and unfixed rotation period. In this paper, a fault detection method of rotary bearing based on improved comprehensive features is proposed for the first time. Firstly, six classical time-domain indexes, namely kurtosis index, margin index, pulse index, peak index, waveform index and root mean square value, are used as the comprehensive indexes of slewing bearing fault detection. Secondly, according to the actual operating status of the door crane, its operating state is divided into normal operating state, not easy to run for a long time and obvious damage state. Combining 6 kinds of time domain characteristics and 3 kinds of operating states, a fault detection model of door crane slewing bearing is formed. Finally, the envelope spectrum method is used to classify the faults, and for the uncertain signal period of the slewing bearing, the periodic detection technology (PDTs) combined with the Maximum Correlated Kurtosis Deconvolution (MCKD) method is used to improve the envelope spectrum method, so as to obtain the fault characteristic frequency in the envelope spectrum more clearly, forming a practical fault detection method of the slewing bearing.