滚动轴承故障特征信号为非平稳瞬态信号，极容易淹没在与转子转速相关的背景信号和其他噪声中，如何从滚动轴承故障信号中分离出故障特征是其故障诊断的关键。提出将相空间重构和平稳子空间分析相结合的滚动轴承故障诊断方法，首先应用相空间重构实现对滚动轴承故障振动信号的升维，然后利用平稳子空间对高维信号中的平稳源信号和非平稳源信号进行区分，并对峭度值最大的非平稳源信号进行最小熵解卷积降噪，最后对降噪信号进行包络谱分析提取轴承故障特征频率。仿真信号和故障诊断实例表明，诊断效果优于基于EMD的包络解调方法。
Abstract
Rolling bearing’s fault feature signals are non-stationary, transient, which are often submerged in the background signals associated with rotor speed and other noise components, how to separate the fault feature signals from the rolling bearing’s blind sources is an important issue. A method combined the phase space reconstruction technique and the stationary subspace analysis (SSA) was proposed. First, the fault vibration signal’s dimension was increased by the phase space technique. Second, the stationary and the non-stationary source components were distinguished by SSA from the multi-dimensional signals. Then, the selected non-stationary component which had the maximum kurtosis value was de-noised by the minimum entropy deconvolution (MED). Finally, the de-noised non-stationary component was analyzed by the envelope spectrum to extract the fault characteristic frequency. The analysis result of simulation and experiment signals indicated that the proposed method could extract the fault frequency better than the envelope demodulation method based on empirical mode decomposition (EMD).
关键词
相空间重构 /
平稳子空间分析 /
最小熵解卷积 /
滚动轴承 /
故障诊断
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Key words
phase space reconstruction /
stationary subspace analysis /
minimum entropy deconvolution ;rolling bearing /
fault diagnosis
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