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西北工业大学 国内
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A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network
Yuantao Yang, Huailiang Zheng,
Yongbo Li
, Minqiang Xu, Yushu Chen
航空学院
Harbin Institute of Technology
科研成果
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98
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探究 'A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network' 的科研主题。它们共同构成独一无二的指纹。
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Computer Science
Convolutional Neural Network
100%
Fault Diagnosis
100%
Feature Extraction
60%
fault diagnose method
40%
Network Architecture
40%
Health Condition
40%
Deep Learning Method
20%
Representation Learning
20%
Superior Performance
20%
Feature Selection
20%
Extracted Feature
20%
Linear Relationship
20%
Operating Condition
20%
Intelligent Fault Diagnosis
20%
Engineering
Fault Diagnosis
100%
Rotating Machinery
100%
Convolutional Neural Network
100%
Feature Extraction
60%
Linear Relationship
20%
Centrifugal Pump
20%
Extracted Feature
20%
Deep Learning Method
20%
Model Diagnosis
20%
Human Labor
20%
Biochemistry, Genetics and Molecular Biology
Feature Extraction
100%
Signal Processing
33%
Chemical Engineering
Neural Network
100%
Deep Learning Method
20%