摘要
针对普通多尺度形态学在滚动轴承微弱故障识别时的结构元素尺度优化问题,提出了自适应多尺度形态学的滚动轴承故障诊断方法。首先,以基本形态算子为基础,构建了具有双向脉冲特征提取能力的差分形态算子;为了在结构元素尺度范围定义出敏感于轴承故障的尺度,将以峭度作为其选取依据,并将得到最优尺度下的形态处理结果平均处理来重构故障信号,最终实现故障信号的综合分析和故障特征解调。通过对实际滚动轴承内、外圈故障振动信号的分析,结果表明:优化算法能够提取出强噪声背景下的滚动轴承微弱故障特征;且相比于普通多尺度形态学,该方法所提取的轴承故障特征频率幅值提升近1倍,适合处理弱信噪比的滚动轴承内、外圈故障识别。
Aiming at the optimization of structural element scale of common multi-scale morphology(CMM)for identifying weak faults in rolling bears,an adaptive multi-scale morphology method is proposed.Firstly,difference operator based on morphological basic operators is established because it can extract bidirectional impulses.In order to define optimal ones in certain scale range that are sensitive to bearing fault feature,kurtosis is considered as basis for the selection,and the final fault signals are reconstructed by averaging the results of the optimized ones,which enables the comprehensive analysis of fault signals and the demodulation of fault characteristics.With vibration signals collected from rolling bearing with the outer and inner ring faults,it is validated that the proposed method is effective to diagnose the two bearing faults under strong noise background.Compared with CMM algorithm,the amplitudes of the extracted feature frequencies are nearly doubled,and it is suitable to identify the inner and outer ring faults of rolling bearings with weak signal-to-noise ratio.
作者
杜文轩
李立玉
何陈诚
龚廷恺
DU Wen-xuan;LI Li-yu;HE Chen-cheng;GONG Ting-kai(School of General Aviation,Nanchang Hangkong University,Nanchang 330063,China;Jiangling Motors Co.,Ltd.,Intelligent Networking Research Institute,Nanchang 330200,China;School of Aircraft Engineering,Nanchang Hangkong University,Nanchang 330063,China)
出处
《失效分析与预防》
2023年第3期173-178,183,共7页
Failure Analysis and Prevention
基金
南昌航空大学博士启动基金(EA202016364)
南昌航空大学创新创业基金(206010220054)。
关键词
自适应多尺度
峭度
故障诊断
滚动轴承
形态滤波
adaptive multiscale
Kurtosis
fault diagnosis
rolling bearing
morphological filtering
