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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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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 electronic resource (236 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
rolling bearing
performance degradation hybrid kernel function krill herd algorithm SVR acoustic-based diagnosis gear fault diagnosis attention mechanism convolutional neural network stacked auto-encoder weighting strategy deep learning bearing fault diagnosis intelligent leak detection acoustic emission signals statistical parameters support vector machine wavelet denoising Shannon entropy adaptive noise reducer gaussian reference signal gearbox fault diagnosis one against on multiclass support vector machine varying rotational speed fault detection and diagnosis faults estimation actuator and sensor fault observer design Takagi-Sugeno fuzzy systems automotive perception sensor lidar fault detection fault isolation fault identification fault recovery fault diagnosis fault detection and isolation (FDIR) autonomous vehicle model predictive control path tracking control fault detection and isolation braking control nonlinear systems fault tolerant control iterative learning control neural networks cryptography wireless sensor networks machine learning scan-chain diagnosis artificial neural network NARX control valve decision tree signature matrix |
| 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 | ||
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