基于改进固有时间尺度分解和谱峭度的轴承故障诊断方法

发布时间：2017-12-27 09:46

本文关键词：基于改进固有时间尺度分解和谱峭度的轴承故障诊断方法　出处：《太阳能学报》2017年03期 　论文类型：期刊论文

【摘要】：基于固有时间尺度分解(intrinsic time-scale decomposition,ITD)方法的线性变换和三次样条插值,提出一种改进的固有时间尺度分解方法(improved intrinsic time-scale decomposition,IITD),将IITD方法和谱峭度(spectrumkurtosis,SK)相结合,实现轴承故障的智能诊断。首先采用改进ITD方法对采集的轴承振动信号进行分解,得到若干个固有旋转分量(proper rotation component,PRC),然后利用谱峭度法对相关性最大的PRC进行滤波处理,最后对滤波后的PRC进行Hilbert包络解调来提取故障特征频率,从而识别轴承故障类型。仿真和实验分析结果表明:该文所提IITD-SK法可成功提取出故障特征频率,实现轴承故障的有效诊断,与传统的傅里叶变换、包络谱分析以及EMD方法的结果相比,该方法诊断效果更佳。
[Abstract]:Based on intrinsic time scale decomposition (intrinsic time-scale decomposition, ITD) linear transformation method and three spline interpolation, put forward a kind of improved intrinsic time scale decomposition method (improved intrinsic time-scale decomposition, IITD), IITD method and spectral kurtosis (spectrumkurtosis, SK) combined to realize intelligent diagnosis of bearing fault. First, using the improved ITD method to decompose the bearing vibration signal acquisition, some proper rotation components (proper, rotation component, PRC), and then use the correlation of the maximum PRC filtering spectral kurtosis, the filtered PRC Hilbert envelope demodulation to extract the fault feature frequency, so as to identify bearing fault type. The simulation and experimental results show that the proposed IITD-SK method can successfully extract the fault feature frequency and achieve effective diagnosis of bearing failure. Compared with the traditional Fourier transform, envelope spectrum analysis and EMD method, the proposed method has better diagnosis effect.
【作者单位】：国网山西省电力公司电力科学研究院;国网山西省电力公司;
【分类号】：TH133.3
【正文快照】： 0引言轴承是机械传动系统的核心部件,在功率传递的过程中发挥着至关重要的作用,如新能源风力发电机主轴承、齿轮箱的各级轴承等[1,2]。一旦轴承发生故障,传动系统的正常运行会受到极大影响。如果能在轴承故障的早期阶段有效提取出故障特征信息,实现轴承运行状态的准确判别,并

【相似文献】