摘要
由于实际旋转机械中转静碰摩故障获取较为困难，而大量拥有的是正常的非碰摩状态样本，为此，本文引入一类支持向量机学习算法进行转静碰摩故障识别，通过对大量正常样本的学习得到碰摩故障判别边界，从而实现碰摩擦故障检测。由于转子故障信号的频谱存在大量冗余，本文又提出一种基于主成分分析的转子故障特征提取方法。该方法首先对信号频谱进行归一化处理后，然后，对大量样本的频谱进行主成分分析，按不同的能量保持率要求提取出低维特征。最后，通过诊断实验表明了本文方法的正确有效性。
Abstract
In practice, it is very difficult to obtain the rubbing fault samples, but the non-rubbing normal samples are very rich, therefore, in this paper the one-class support vector machine(SVM) is introduced to recognize the rubbing fault, which can obtain the recognition border of rubbing fault through learning from only the a lot of normal samples. Because the rotor fault signal spectrum features are very redundant, a new feature extraction method based on the Primary Component Analysis (PCA) is put forward, firstly, the rotor fault signal frequency spectrum is normalized; secondly, the spectra data of a lot of samples is carry out Primary Component Analysis, and the lower dimensions features are extracted according to different energy preserving rate. Finally, the new approach is verified through some diagnosis experiments.
关键词
转子 /
碰摩 /
故障诊断 /
频谱 /
一类支持向量机 /
主成分分析 /
特征提取
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Key words
Rotor /
Fault diagnosis /
Frequency spectrum /
Primary component analysis (PCA) /
Feature extraction
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陈 果.
基于一类支持向量机与主成分分析的转静碰摩故障检测技术[J]. 振动与冲击, 2012, 31(22): 29-33
CHEN Guo .
Rotor-Stator Rubbing Fault Testing Technique Based on One-Class Support Vector Machines and Primary Component Analysis CHEN Guo [J]. Journal of Vibration and Shock, 2012, 31(22): 29-33
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