Deep Learning-Based Machinery Fault Diagnostics

• Autore: Chen Hongtian
• Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (290 p.)
• Soggetto topico: History of engineering & technology; Technology: general issues
• Soggetto non controllato: abnormal case removal; alumina concentration; aluminum reduction process; anti-noise; attention mechanism; autonomous underwater vehicle; auxiliary model; Bayesian network; bearing fault detection; belief rule base; canonical correlation analysis; canonical variate analysis; case-based reasoning; convolution fusion; convolutional neural network; data augmentation; data-driven; deep residual network; distributed predictive control; disturbance detection; dynamic autoregressive latent variables model; dynamics; event-triggered control; evidential reasoning; evidential reasoning rule; fault detection; fault diagnosis; fault tolerant control; filter; flywheel fault diagnosis; fractional-order calculus theory; fuzzy fault tree analysis; gated recurrent unit; gearbox fault diagnosis; hammerstein output-error systems; high-speed trains; information transformation; intelligent fault diagnosis; interval type-2 Takagi-Sugeno fuzzy model; just-in-time learning; k-nearest neighbor analysis; local outlier factor; LSSVM; multi-innovation identification theory; n/a; nonlinear networked systems; ocean currents; operational optimization; parameter optimization; power transmission system; process monitoring; PSO; robust optimization; sintering process; spatiotemporal feature fusion; stacked pruning sparse denoising autoencoder; state identification; statistical local analysis; subspace identification; system modelling; thruster fault diagnostics; variable time lag; wavelet mutation
• Persona (resp. second.): ZhongKai; RanGuangtao; ChengChao; ChenHongtian
• Sommario/riassunto: 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.
• Titolo autorizzato: Deep Learning-Based Machinery Fault Diagnostics
• ISBN: 3-0365-5174-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910619469103321
• Lo trovi qui: Univ. Federico II