一种采煤机截割部滚动轴承故障诊断方法

A fault diagnosis method for rolling bearing of shearer cutting section

摘要:

针对采煤机截割部滚筒轴承结构复杂极易损坏，且单凭借人为经验难以对故障进行定位判别的问题，提出了一种基于入人工蚁群-模拟退火算法的自适应时变滤波经验模态分解方法，去掉了原始振动信号中的低频干扰，画信号快速峭度图以峭度最大处对应的中心频率和带宽来设定带通滤波器参数，以进一步突出信号的高频特征，对经过处理后的信号平方包络后画包络谱图，将计算所得理论故障频率和包络谱图最大幅值处所对应的频率对比，从而对故障类型进行判别；通过采煤机试验表明所提方法能够适用于采煤机实际工程应用，可以有效消除信号中低频干扰部分，提高振动信号的峭度特征，并在不同转速和不同故障尺寸的条件下下分别达到100%和88.9%的故障判别率。

Abstract:

The roller bearing of the cutting part of shearer has a complex structure and is easy to be damaged, and it is difficult to locate and judge the fault based on human experience alone. An empirical mode decomposition method of adaptive time-varying filtering based on worker ant colony-simulated annealing algorithm is proposed to remove the low frequency interference in the original vibration signal. The band-pass filter parameters are set with the center frequency and bandwidth corresponding to the maximum kurtosis, so as to further highlight the high frequency characteristics of the signal. The envelope spectrum diagram is drawn after the square envelope of the processed signal, and the calculated theoretical fault frequency is compared with the frequency corresponding to the maximum value of the envelope spectrum, so as to distinguish the fault type. Shearer tests show that the proposed method can be applied to practical shearer engineering applications, effectively eliminate the low-frequency interference part of the signal, improve the kurtosis characteristic of vibration signal, and achieve the fault discrimination rate of 100% and 88.9% under different speed and different fault size conditions.