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Fault identification using finite element models and neural networks
T. Marwala
, H. E.M. Hunt
University of Cambridge
Research output
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Contribution to journal
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Article
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peer-review
81
Citations (Scopus)
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Dive into the research topics of 'Fault identification using finite element models and neural networks'. Together they form a unique fingerprint.
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Computer Science
fault identification
100%
Neural Network
100%
Frequency Response
100%
Finite Element Method
100%
Response Function
66%
Function Parameter
33%
Modal Parameter
33%
Simulated Data
33%
Engineering
Frequency Response Function
100%
Finite Element Modeling
100%
Simulated Data
50%
Modal Parameter
50%
Mean Square Error
50%
Frequency Response
50%
Noise Level
50%
Mathematics
Finite Element Method
100%
Neural Network
100%
Frequency Response Function
66%
Minimizes
33%
Mean Square Error
33%
Standard Deviation
33%
Simulated Data
33%
Frequency Response
33%
Keyphrases
Property-based
50%
Function Parameters
50%
Modal Data
50%
Equal Weights
50%
Response-based Method
50%
Noise Level
50%
Chemical Engineering
Neural Network
100%
Material Science
Finite Element Modeling
100%