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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 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 |
| 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 | ||
| ||