摘要
针对滚动轴承故障振动信号的非平稳特征和故障征兆模糊性,提出了基于EMD和动态模糊聚类图的轴承故障诊断方法。运用EMD方法提取待诊断的轴承运行状态样本的能量特征指标,应用模糊聚类分析方法对特征参数进行聚类,并作出聚类树状图。结果表明,该方法不需要大量的样本进行学习,且能更直观、准确识别滚动轴承的运行状态。
For the non-stationary feature of a vibration signal of defective rolling bearings and the ambiguity of fault feature, a fault diagnosis method of rolling bearings is proposed using EMD （ Empirical Mode Decomposition ）, Dynamic fuzzy clustering graph. Firstly, an EMD method was used to decompose a vibration signal of a rolling bearing. Then those parameters were analyzed by fuzzy clustering algorithm, and plotted amic fuzzy clustering graph. Experiments indicated that This method does not require a large number of samples for leaming, and And can more intuitivelt, accurately distinguish the running state of bearings.
出处
《机械》
2012年第7期1-4,共4页
Machinery
基金
国家自然科学基金资助项目(51175511)
关键词
EMD分解
动态模糊聚类图
故障诊断
emp iricalmode decomposition （ EMD ）
dynamic fuzzy clustering graph
fault diagnosis
