Deep Learning-Based Machinery Fault Diagnostics

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular in...

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Bibliographic Details
HerausgeberIn:
Zhong, Kai
Ran, Guangtao
Cheng, Chao
Sonstige:
Chen, Hongtian
Zhong, Kai
Ran, Guangtao
Cheng, Chao
Year of Publication:2022
Language:English
Physical Description:1 electronic resource (290 p.)
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id 993562968204498
ctrlnum (CKB)5670000000391583
(oapen)https://directory.doabooks.org/handle/20.500.12854/93169
(EXLCZ)995670000000391583
collection bib_alma
record_format marc
spelling Chen, Hongtian edt
Deep Learning-Based Machinery Fault Diagnostics
MDPI - Multidisciplinary Digital Publishing Institute 2022
1 electronic resource (290 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis.
English
Technology: general issues bicssc
History of engineering & technology bicssc
process monitoring
dynamics
variable time lag
dynamic autoregressive latent variables model
sintering process
hammerstein output-error systems
auxiliary model
multi-innovation identification theory
fractional-order calculus theory
canonical variate analysis
disturbance detection
power transmission system
k-nearest neighbor analysis
statistical local analysis
intelligent fault diagnosis
stacked pruning sparse denoising autoencoder
convolutional neural network
anti-noise
flywheel fault diagnosis
belief rule base
fuzzy fault tree analysis
Bayesian network
evidential reasoning
aluminum reduction process
alumina concentration
subspace identification
distributed predictive control
spatiotemporal feature fusion
gated recurrent unit
attention mechanism
fault diagnosis
evidential reasoning rule
system modelling
information transformation
parameter optimization
event-triggered control
interval type-2 Takagi-Sugeno fuzzy model
nonlinear networked systems
filter
gearbox fault diagnosis
convolution fusion
state identification
PSO
wavelet mutation
LSSVM
data-driven
operational optimization
case-based reasoning
local outlier factor
abnormal case removal
bearing fault detection
deep residual network
data augmentation
canonical correlation analysis
just-in-time learning
fault detection
high-speed trains
autonomous underwater vehicle
thruster fault diagnostics
fault tolerant control
robust optimization
ocean currents
3-0365-5173-5
Zhong, Kai edt
Ran, Guangtao edt
Cheng, Chao edt
Chen, Hongtian oth
Zhong, Kai oth
Ran, Guangtao oth
Cheng, Chao oth
language English
format eBook
author2 Zhong, Kai
Ran, Guangtao
Cheng, Chao
Chen, Hongtian
Zhong, Kai
Ran, Guangtao
Cheng, Chao
author_facet Zhong, Kai
Ran, Guangtao
Cheng, Chao
Chen, Hongtian
Zhong, Kai
Ran, Guangtao
Cheng, Chao
author2_variant h c hc
k z kz
g r gr
c c cc
author2_role HerausgeberIn
HerausgeberIn
HerausgeberIn
Sonstige
Sonstige
Sonstige
Sonstige
title Deep Learning-Based Machinery Fault Diagnostics
spellingShingle Deep Learning-Based Machinery Fault Diagnostics
title_full Deep Learning-Based Machinery Fault Diagnostics
title_fullStr Deep Learning-Based Machinery Fault Diagnostics
title_full_unstemmed Deep Learning-Based Machinery Fault Diagnostics
title_auth Deep Learning-Based Machinery Fault Diagnostics
title_new Deep Learning-Based Machinery Fault Diagnostics
title_sort deep learning-based machinery fault diagnostics
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2022
physical 1 electronic resource (290 p.)
isbn 3-0365-5174-3
3-0365-5173-5
illustrated Not Illustrated
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status_str n
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carrierType_str_mv cr
is_hierarchy_title Deep Learning-Based Machinery Fault Diagnostics
author2_original_writing_str_mv noLinkedField
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