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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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Smart Sensors and Devices in Artificial Intelligence
| Smart Sensors and Devices in Artificial Intelligence |
| Autore | Zhang Dan |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (336 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
adaptive hedge algorithm
adaptive landing adverse weather air pollution air quality monitoring AIS ankle-foot exoskeleton artificial intelligence artificial neural network atmospheric data audification autonomous vehicle banana ripening cable tension measurement clamping force estimation CNN CO2 welding color histogram correlation filter data augmentation data fusion data processing data streams deep learning DenseNet distributed energy-efficient ethylene gas evacuation path FBG fruit condition monitoring fuzzy functional dependencies HF-OTH radar inertial measurement unit IoT joint torque disturbance observer landing gear link establishment behaviors local outlier factor long short term memory recurrent neural networks LSTM machine learning maritime surveillance mechanical sensor microelectromechanical systems molten pool multi-story multi-exit building multi-time-slots planning n/a online monitoring optimization outlier detection PSO-BPNN queue length radar tracking reliable RNN roadside LiDAR roadside sensor scenario generation self-adaptiveness sensor Sensors short-wave radio station signal recognition smart cities smart sensor and device smart sensors speech to text surgical robot end-effector task assignment temperature sensors text to speech traffic flow traffic forecasting unidimensional ACGAN vehicle classification vehicle detection visual tracking visualization walking assistance wireless sensor network wireless sensor networks |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557128403321 |
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Zhang Dan
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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