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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
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modeling, Optimization and Design Method of Metal Manufacturing Processes
| Modeling, Optimization and Design Method of Metal Manufacturing Processes |
| Autore | Zhang Guoqing |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (214 p.) |
| Soggetto topico |
Business strategy
Manufacturing industries |
| Soggetto non controllato |
machine learning
reinforcement learning Q-learning steelmaking process CAS-OB decision-support system optimisation algorithm 3D auxetic structures selective laser melting micro assembled structural surface layer model A380 alloy Ca AlFeSi phase refine micro-cutting grain size surface integrity cutting forces chip formation OFHC copper C102 amorphous alloys Fe-based amorphous alloys difficult-to-machine assisted machining high-frequency PCB drilling coating technology tool wear hot filament chemical vapor deposition PCBN tool gray cast iron surface quality temperature prediction weighted regularized extreme learning machine just-in-time learning sample similarities variable correlations tool edge preparation orthogonal cutting numerical simulation ANOVA temperature stress ECAP metallic materials processing parameters deformation mechanism |
| ISBN | 3-0365-6033-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910639996303321 |
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Zhang Guoqing
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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