top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.
Data-driven detection and diagnosis of faults in traction systems of high-speed trains / / Hongtian Chen, Bin Jiang, Ningyun Lu, Wen Chen
Data-driven detection and diagnosis of faults in traction systems of high-speed trains / / Hongtian Chen, Bin Jiang, Ningyun Lu, Wen Chen
Autore Chen Hongtian
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (xiii, 160 pages) : illustrations
Disciplina 385.22
Collana Lecture Notes in Intelligent Transportation and Infrastructure
Soggetto topico Fault location (Engineering)
High speed trains
ISBN 3-030-46263-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Traction Systems and Experimental Platforms -- Basics of Data-driven FDD Methods -- Multi-mode PCA-based FDD Methods -- Probability-relevant PCA-based FDD Methods -- Deep PCA-based FDD Methods -- PCA and Kull back-Leibler Divergence-based FDD Methods -- PCA and Hellinger Distance-based FDD Methods -- Conclusions and Further Work.
Record Nr. UNINA-9910392740503321
Chen Hongtian  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Deep Learning-Based Machinery Fault Diagnostics
Deep Learning-Based Machinery Fault Diagnostics
Autore Chen Hongtian
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica 1 electronic resource (290 p.)
Soggetto topico Technology: general issues
History of engineering & technology
Soggetto non controllato 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
ISBN 3-0365-5174-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910619469103321
Chen Hongtian  
MDPI - Multidisciplinary Digital Publishing Institute, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui