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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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Sensors Fault Diagnosis Trends and Applications
| Sensors Fault Diagnosis Trends and Applications |
| Autore | Witczak Piotr |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (236 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
acoustic emission signals
acoustic-based diagnosis actuator and sensor fault adaptive noise reducer artificial neural network attention mechanism automotive autonomous vehicle bearing fault diagnosis braking control control valve convolutional neural network cryptography decision tree deep learning fault detection fault detection and diagnosis fault detection and isolation fault detection and isolation (FDIR) fault diagnosis fault identification fault isolation fault recovery fault tolerant control faults estimation gaussian reference signal gear fault diagnosis gearbox fault diagnosis hybrid kernel function intelligent leak detection iterative learning control krill herd algorithm lidar machine learning model predictive control n/a NARX neural networks nonlinear systems observer design one against on multiclass support vector machine path tracking control perception sensor performance degradation rolling bearing scan-chain diagnosis Shannon entropy signature matrix stacked auto-encoder statistical parameters support vector machine SVR Takagi-Sugeno fuzzy systems varying rotational speed wavelet denoising weighting strategy wireless sensor networks |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557506603321 |
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Witczak Piotr
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||