针对滚动轴承故障信号因受背景噪声、信号传递途径、轴承各部件间相互作用及其它能量较大振源信号干扰，限制传统方法提取故障特征信息的准确性问题，提出结合固有时间尺度分解（ITD）及独立分量分析（ICA）的信号分析方法，将单通道振动信号进行ITD分解，得到若干固有旋转分量及一个趋势项，基于互相关准则对分解信号进行重组作为ICA的输入矩阵，采用FastICA算法解混，实现故障特征信号与噪声信号分离，从而提取故障特征信息。通过滚动轴承故障诊断实验结果分析表明该方法有效可行，具有一定工程应用价值。
Abstract
Rolling bearing fault signals appear mostly in the form of modulation, and are vulnerable to influenced by other big energy source signals，causing the great limitation of traditional method on information extraction. According to these characteristics, the intrinsic time scale decomposition (ITD) and independent component analysis (ICA) of signal analysis method was proposed. Firstly, the signal was decomposed into several proper rotation components and a trend component by the ITD method. Then they were destructed as the input matrix of ICA based on mutual correlation criterion. Using FastICA algorithm to solve mixed, so as to realize the separation of the fault signal and the noise signal. This method is applied to the fault diagnosis of rolling bearing. The analysis of the field data results show that this method is effective and feasible, and also has certain engineering application value.
关键词
固有时间尺度分析 /
独立分量分析 /
滚动轴承 /
故障诊断
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Key words
intrinsic time scale decomposition /
independent component analysis /
rolling bearing /
fault diagnosis
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