Sample Space Based on Multi-level High Dimensional Feature Representation Micro-fault Diagnosis

被引：0

作者：

Zhang C.-X. ^{[1
,3
]}

Wang Z.-H. ^{[2
]}

Wen C.-L. ^{[2
]}

Liu G.-W. ^{[1
,3
]}

Yu W. ^{[1
,3
]}

机构：

[1] School of Machatronic Engineering and Automation, Foshan University, Foshan, 528000, Guangdong

[2] Automatic School, Hangdian University, Hangzhou, 310018, Zhejiang

[3] Guangdong Province Smart City Infrastructure Health Monitoring and Evaluation Engineering Technology Research Center, Foshan, 528000, Guangdong

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 08期

关键词：

Fault diagnosis; Multilevel high-dimensional; Principal component analysis; Projection frame;

D O I：

10.3969/j.issn.0372-2112.2020.08.026

中图分类号：

学科分类号：

摘要：

Traditional principal component analysis, relative principal component analysis and other multivariate statistical methods based on threshold to do the fault diagnosis.Since multivariate statistical method is an equivalent representation of the original space, it does not add any amount of information, making it difficult to diagnose minor faults.And the original space is reduced dimensionally into the principal component space and the residual space, so that the tiny information cannot be fully expressed.Deep learning has been successfully applied in pattern recognition.However, multilevel networks of deep learning represent linear combinations of details but do not have explanatory.Only with the result of training without theoretical basis, it is difficult to carry out mechanistic analysis.This paper presents a fault diagnosis method which combines PCA thought and deep learning thought.Based on the original PCA, this paper first expands and then reduces the dimension, making the inexplicit information in the original space fully expressed and interpreted.Theoretical and simulation experiments show that this method can judge the minor faults which cannot be detected by traditional PCA, improve the detection rate of fault detection and have interpretability. © 2020, Chinese Institute of Electronics. All right reserved.

引用

页码：1647 / 1654

页数：7

共 20 条

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