Yang et al., 2021 - Google Patents
Crack detection in carbide anvil using acoustic signal and deep learning with particle swarm optimisationYang et al., 2021
- Document ID
- 4903564585548358169
- Author
- Yang J
- Chen B
- Wang Y
- Wang C
- Publication year
- Publication venue
- Measurement
External Links
Snippet
The carbide anvil plays a significant role in producing synthetic diamond. However, it suffers from complex alternating stresses and consequently results in fatigue damage such as cracks. Accurate crack detection of the carbide anvil still faces a significant challenge. This …
- 238000001514 detection method 0 title abstract description 29
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/46—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by spectral analysis, e.g. Fourier analysis or wavelet analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN112629863B (en) | Bearing fault diagnosis method for dynamic joint distribution alignment network under variable working conditions | |
Guo et al. | An unsupervised feature learning based health indicator construction method for performance assessment of machines | |
Lu et al. | Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition | |
Yang et al. | Crack detection in carbide anvil using acoustic signal and deep learning with particle swarm optimisation | |
Liu et al. | Structure damage diagnosis using neural network and feature fusion | |
Pan et al. | A deep learning network via shunt-wound restricted Boltzmann machines using raw data for fault detection | |
CN104899327B (en) | A kind of time series method for detecting abnormality of no class label | |
Shu et al. | A multi-task learning-based automatic blind identification procedure for operational modal analysis | |
CN113865868B (en) | Rolling bearing fault diagnosis method based on time-frequency domain expression | |
CN108830328B (en) | Microseismic signal SMOTE identification method and monitoring system fusing spatial knowledge | |
CN109409271B (en) | Ferromagnetic material hardness prediction algorithm based on BP neural network improved algorithm | |
CN114487129B (en) | Flexible material damage identification method based on acoustic emission technology | |
CN113609789A (en) | Cutter wear state prediction method based on space-time feature parallel extraction | |
CN105241665A (en) | Rolling bearing fault diagnosis method based on IRBFNN-AdaBoost classifier | |
Moeinifard et al. | Lost vibration test data recovery using convolutional neural network: a case study | |
Medina et al. | Deep learning-based gear pitting severity assessment using acoustic emission, vibration and currents signals | |
CN110688983A (en) | Microseismic signal identification method based on multi-mode optimization and ensemble learning | |
CN115185937A (en) | SA-GAN architecture-based time sequence anomaly detection method | |
Sony | Towards multiclass damage detection and localization using limited vibration measurements | |
Li et al. | Intelligent fault diagnosis of rotating machinery based on deep recurrent neural network | |
CN110222386A (en) | A kind of planetary gear degenerate state recognition methods | |
He et al. | Uncertainty quantification in multiaxial fatigue life prediction using Bayesian neural networks | |
Fu et al. | Automatic bolt tightness detection using acoustic emission and deep learning | |
Shanling et al. | Real-time rubber quality model based on CNN-LSTM deep learning theory | |
Chen et al. | Gear Fault Diagnosis Under Variable Load Conditions Based on Acoustic Signals |