Deep Learning-Based Machinery Fault Diagnostics
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular in...
Guardado en:
| Formato: | Online |
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| Lenguaje: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Materias: | |
| Acceso en línea: | ONIX_20221025_9783036551739_23 |
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| _version_ | 1869521821835984896 |
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| collection | Directory of Open Access Books |
| description | This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis. |
| format | Online |
| id | doab-20.500.12854ir-93169 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-931692024-04-09T23:16:07Z Deep Learning-Based Machinery Fault Diagnostics Chen, Hongtian Zhong, Kai Ran, Guangtao Cheng, Chao 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 n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis. 2022-10-25T09:00:10Z 2022-10-25T09:00:10Z 2022 book ONIX_20221025_9783036551739_23 9783036551739 9783036551746 https://directory.doabooks.org/handle/20.500.12854/93169 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/6065 https://mdpi.com/books/pdfview/book/6065 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5174-6 10.3390/books978-3-0365-5174-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036551739 9783036551746 290 open access |
| spellingShingle | 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 n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Deep Learning-Based Machinery Fault Diagnostics |
| title | Deep Learning-Based Machinery Fault Diagnostics |
| title_full | Deep Learning-Based Machinery Fault Diagnostics |
| title_fullStr | Deep Learning-Based Machinery Fault Diagnostics |
| title_full_unstemmed | Deep Learning-Based Machinery Fault Diagnostics |
| title_short | Deep Learning-Based Machinery Fault Diagnostics |
| title_sort | deep learning based machinery fault diagnostics |
| topic | 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 n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | 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 n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20221025_9783036551739_23 |