Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis:

Shuangyuan Wang; Yixiang Huang; Liang Gong; Lin Li; Chengliang Liu

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Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis:

机译：使用结构化Fisher稀疏编码方案改进的特征提取用于机械故障诊断：

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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 ...

机译：反映各种机械状况的振动信号对于故障诊断非常有用。但是，振动信号的特性对于不同类型的设备和故障模式而言并不相同。在无结构状态诊断模型中，这些可用信息通常会丢失。我们提出了一种基于结构化Fisher稀疏编码的故障诊断方案，以兼顾效率和有效性来改进特征提取过程。它由三个主要部分组成：（1）用于合成通过结构Fisher鉴别字典学习获得的振动信号的结构化字典;（2）树形结构的稀疏编码，用于从振动信号中提取稀疏表示系数以表示故障特征;以及（3）支持向量机的分类器对特征进行识别，以识别不同的故障。通过标准轴承故障数据集和蜗轮蜗杆故障实验验证了该算法的有效性。测试结果证明...

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《Advances in Mechanical Engineering》 |2016年第12期|共16页
作者
Shuangyuan Wang; Yixiang Huang; Liang Gong; Lin Li; Chengliang Liu;
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中图分类机械、仪表工业;
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1. Adaptive feature extraction using sparse coding for machinery fault diagnosis [J] . Haining Liu, Chengliang Liu, Yixiang Huang Mechanical systems and signal processing . 2011,第2期

机译：基于稀疏编码的自适应特征提取在机械故障诊断中的应用
2. Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis [J] . Li Jimeng, Yao Xifeng, Wang Hui, Mechanical systems and signal processing . 2019,第JULa1期

机译：基于改进的VMD和稀疏码收缩降噪的周期脉冲提取及其在旋转机械故障诊断中的应用。
3. Sparse feature extraction for fault diagnosis of rotating machinery based on sparse decomposition combined multiresolution generalized S transform [J] . Baokang Yan, Bin Wang, Fengxing Zhou, Journal of low frequency noise, vibration and active control . 2019,第2期

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4. Chapter 43 Fault Diagnosis of Rolling Bearing Based on Fisher Discrimination Sparse Coding [C] . Chengliang Li, Zhongsheng Wang, Chan Ding Symposium on aviation maintenance and management . 2014

机译：第43章基于Fisher鉴别稀疏编码的滚动轴承故障诊断
5. Rotating machinery monitoring: Feature extraction, signal separation, and fault severity evaluation. [D] . Hong, Hoonbin. 2007

机译：旋转机械监控：特征提取，信号分离和故障严重性评估。
6. A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm [O] . Wenlong Fu, Jiawen Tan, Chaoshun Li, 2018

机译：一种旋转机械的混合故障诊断方法通过混沌量子正弦余弦算法优化了基于熵的特征提取和SVM的融合
7. Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis [O] . Shuangyuan Wang, Yixiang Huang, Liang Gong, 2016

机译：采用结构性渔业辨别稀疏编码方案改进的特征提取用于机械故障诊断

Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis:

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