t分布随机近邻嵌入机械故障特征提取方法研究

谷玉海; 韩秋实; 徐小力; 高鹏

doi:10.13433/j.cnki.1003-8728.2016.1216

t分布随机近邻嵌入机械故障特征提取方法研究

doi: 10.13433/j.cnki.1003-8728.2016.1216

1.
机械科学研究总院, 北京 100044;
2.
北京信息科技大学现代测控教育部重点实验室, 北京 100192

基金项目:

国家自然科学基金项目（51275052）与北京市自然科学基金重点项目（3131002）资助

详细信息

作者简介:
谷玉海(1976-),副研究员,博士,研究方向机械故障诊断,guyuhai@bistu.edu.cn

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- 文章访问数: 128
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- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2015-04-15
- 刊出日期: 2017-01-05

An Early Fault Feature Extraction Method Based on t-Distribution Stochastic Neighbor Embedding for Large Rotating Machinery

1.
China Academy of Machinery Science & Technology, Beijing 100044, China;
2.
The Ministry of Education Key Laboratory of Modern Measurement and Control, Beijing Information Science and Technology University, Beijing 100192, China

摘要

摘要: 将t分布随机近邻嵌入（t-SNE）流形学习方法应用于机械振动信号的故障特征提取，实现高维特征信息降维处理。通过小波包分解算法将原始振动信号分解为多层小波子空间，通过计算各层的小波阈值熵构造高维特征数据，然后采用t-SNE方法对构造的高维特征数据进行数据降维，获取低维故障特征信息。采用本特利转子试验台进行故障仿真实验，对采集获得的几种典型故障状态下的振动数据分别基于小波包阈值熵及统计特征构造2组高维数据，并对2组高维特征数据分别采用t-SNE方法进行数据降维处理获得其二维特征数据，通过对比验证了基于小波包阈值熵法构造高维数据后进行t-SNE数据降维的特征提取方法能够更有效的区分故障特征。
- 小波包阈值熵 /
- 流形学习 /
- t-SNE /
- 特征提取
Abstract: A manifold learning method of t-distributed stochastic neighbor embedding(t-SNE) is used in fault feature extraction of mechanical vibration signal to realize the dimension reduction of high-dimensional feature information. The acquired vibration signal is decomposed into wavelet subspaces by wavelet packet decomposition algorithm, and high dimensional feature vector is construct by the calculation of wavelet threshold entropy of every subspace, and then the stochastic neighbor embedding method based on t distribution is applied to reduce the constructed high-dimensional feature vector to low dimensional vector to acquire the features of fault. Fault simulation experiment on Bently rotor kit has been implemented, and two groups of high dimensional data vectors were been constructed by statistical parameter and wavelet package threshold entropy with the acquired vibration signals, and two groups of high dimensional vectors were reduced to 2 dimension vector by t-SNE method. By comparing, we validate that the extraction method of t-SNE dimension reduction of data based on high dimensional data constructed by Wavelet Packet Threshold Entropy is effective in distinguishing the fault features.
- wavelet package threshold entropy /
- manifold learning /
- t-SNE /
- feature extraction

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参考文献(15)

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