Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis
This Reprint presents the latest research progress of signal processing and artificial intelligence technologies for fault diagnosis of high-end mechanical equipment. It contains 13 original research papers, covering advanced dynamics modeling and mechanism analysis methods, novel signal processing...
Sparad:
| Materialtyp: | Online |
|---|---|
| Språk: | engelska |
| Utgiven: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
|
| Ämnen: | |
| Länkar: | https://directory.doabooks.org/handle/20.500.12854/170645 |
| Taggar: |
Inga taggar, Lägg till första taggen!
|
| _version_ | 1869528006881443840 |
|---|---|
| collection | Directory of Open Access Books |
| description | This Reprint presents the latest research progress of signal processing and artificial intelligence technologies for fault diagnosis of high-end mechanical equipment. It contains 13 original research papers, covering advanced dynamics modeling and mechanism analysis methods, novel signal processing and artificial intelligence methods for machine fault diagnosis using various sensing technologies, and their multi-field applications. These papers mainly report research outcomes in mechanical engineering and its interdisciplinary fields, covering different themes including fault mechanism analysis; structural health monitoring; fault diagnosis and remaining useful life prediction of rotating machinery; high-end mechanical equipment in different industrial fields including the railway, wind energy, machinery manufacturing, and aerospace industries; and different sensing technologies including vibration, temperature, thermal imaging, electrical signals, and force or torque. The Reprint highlights how different sensing technologies could be integrated with advanced signal processing and artificial intelligence technologies for fault diagnosis of industrial mechanical systems. The research results of these interdisciplinary applications will contribute to better understanding of the mechanisms of machine fault diagnosis and remaining useful life prediction, and the application of these advanced monitoring and diagnostic technologies will improve the safety and reliability of mechanical systems in various industrial fields. |
| format | Online |
| id | doab-20.500.12854ir-170645 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1706452026-01-02T16:20:37Z Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis Chen, Bingyan Cheng, Yao data fusion axle box bearing fault diagnosis characteristic frequency bandpass filtering industrial collaborative robot anomaly detection trajectory change autoencoder dynamic time warping online monitoring dual-rotor system rotor unbalance assembly phase angle vibration amplitude aero-engines dynamic modeling aero-engine neural network vibration response fault detection LSTM auto-encoder self-attention mechanism rolling mill abnormal vibration monitoring and diagnosis multi-sensor information fusion multi-scale graph convolutional networks infrared thermography wind turbine blade deep learning defect detection object localization YOLO resonant frequencies autogram optimal demodulation frequency band bearing fault diagnosis rolling bearing convolutional neural network variational Bayesian inference decision-level fusion multi-task learning graph convolutional networks attention mechanism hydrodynamic bearing nonlinear dynamic coefficients oil film instability condition monitoring life prediction transformer dual-layer self-attention multi-scale convolution traction converter temperature sensor coupling fault diagnosis This Reprint presents the latest research progress of signal processing and artificial intelligence technologies for fault diagnosis of high-end mechanical equipment. It contains 13 original research papers, covering advanced dynamics modeling and mechanism analysis methods, novel signal processing and artificial intelligence methods for machine fault diagnosis using various sensing technologies, and their multi-field applications. These papers mainly report research outcomes in mechanical engineering and its interdisciplinary fields, covering different themes including fault mechanism analysis; structural health monitoring; fault diagnosis and remaining useful life prediction of rotating machinery; high-end mechanical equipment in different industrial fields including the railway, wind energy, machinery manufacturing, and aerospace industries; and different sensing technologies including vibration, temperature, thermal imaging, electrical signals, and force or torque. The Reprint highlights how different sensing technologies could be integrated with advanced signal processing and artificial intelligence technologies for fault diagnosis of industrial mechanical systems. The research results of these interdisciplinary applications will contribute to better understanding of the mechanisms of machine fault diagnosis and remaining useful life prediction, and the application of these advanced monitoring and diagnostic technologies will improve the safety and reliability of mechanical systems in various industrial fields. 2026-01-02T16:20:34Z 2026-01-02T16:20:34Z 2025 book 978-3-7258-4961-1 https://directory.doabooks.org/handle/20.500.12854/170645 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11373 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4962-8 10.3390/books978-3-7258-4962-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 978-3-7258-4961-1 290 CH open access |
| spellingShingle | data fusion axle box bearing fault diagnosis characteristic frequency bandpass filtering industrial collaborative robot anomaly detection trajectory change autoencoder dynamic time warping online monitoring dual-rotor system rotor unbalance assembly phase angle vibration amplitude aero-engines dynamic modeling aero-engine neural network vibration response fault detection LSTM auto-encoder self-attention mechanism rolling mill abnormal vibration monitoring and diagnosis multi-sensor information fusion multi-scale graph convolutional networks infrared thermography wind turbine blade deep learning defect detection object localization YOLO resonant frequencies autogram optimal demodulation frequency band bearing fault diagnosis rolling bearing convolutional neural network variational Bayesian inference decision-level fusion multi-task learning graph convolutional networks attention mechanism hydrodynamic bearing nonlinear dynamic coefficients oil film instability condition monitoring life prediction transformer dual-layer self-attention multi-scale convolution traction converter temperature sensor coupling fault diagnosis Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis |
| title | Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis |
| title_full | Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis |
| title_fullStr | Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis |
| title_full_unstemmed | Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis |
| title_short | Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis |
| title_sort | signal processing and artificial intelligence technology for high end equipment fault diagnosis |
| topic | data fusion axle box bearing fault diagnosis characteristic frequency bandpass filtering industrial collaborative robot anomaly detection trajectory change autoencoder dynamic time warping online monitoring dual-rotor system rotor unbalance assembly phase angle vibration amplitude aero-engines dynamic modeling aero-engine neural network vibration response fault detection LSTM auto-encoder self-attention mechanism rolling mill abnormal vibration monitoring and diagnosis multi-sensor information fusion multi-scale graph convolutional networks infrared thermography wind turbine blade deep learning defect detection object localization YOLO resonant frequencies autogram optimal demodulation frequency band bearing fault diagnosis rolling bearing convolutional neural network variational Bayesian inference decision-level fusion multi-task learning graph convolutional networks attention mechanism hydrodynamic bearing nonlinear dynamic coefficients oil film instability condition monitoring life prediction transformer dual-layer self-attention multi-scale convolution traction converter temperature sensor coupling fault diagnosis |
| topic_facet | data fusion axle box bearing fault diagnosis characteristic frequency bandpass filtering industrial collaborative robot anomaly detection trajectory change autoencoder dynamic time warping online monitoring dual-rotor system rotor unbalance assembly phase angle vibration amplitude aero-engines dynamic modeling aero-engine neural network vibration response fault detection LSTM auto-encoder self-attention mechanism rolling mill abnormal vibration monitoring and diagnosis multi-sensor information fusion multi-scale graph convolutional networks infrared thermography wind turbine blade deep learning defect detection object localization YOLO resonant frequencies autogram optimal demodulation frequency band bearing fault diagnosis rolling bearing convolutional neural network variational Bayesian inference decision-level fusion multi-task learning graph convolutional networks attention mechanism hydrodynamic bearing nonlinear dynamic coefficients oil film instability condition monitoring life prediction transformer dual-layer self-attention multi-scale convolution traction converter temperature sensor coupling fault diagnosis |
| url | https://directory.doabooks.org/handle/20.500.12854/170645 |