Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis
Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis
复制标题
使用结构化 Fisher 判别稀疏编码方案改进特征提取用于机械故障诊断
DOI：
10.1177/1687814016683085
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发表时间：
2016-12
影响因子：
2.1
通讯作者：
Chengliang Liu
中科院分区：
文献类型：
--
作者：
Yixiang Huang;Liang Gong;Lin Li;Chengliang Liu
反映不同机械状态的振动信号对故障诊断非常有用。然而，不同类型的设备和故障模式的振动信号特征是不一样的。这些可用信息在无结构状态诊断模型中经常丢失。提出了一种基于结构化Fisher判别稀疏编码的故障诊断方案，以提高特征提取的效率和有效性。该方法主要由三个部分组成：(1)通过结构Fisher判别字典学习得到的用于合成振动信号的结构化字典；(2)从振动信号中提取稀疏表示系数来表示故障特征的树结构稀疏编码；(3)基于特征的支持向量机分类器来识别不同的故障。通过标准轴承故障数据集和蜗轮故障实验验证了该算法的有效性。实验结果表明，该方法具有较好的效率和泛化能力。
Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.
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