Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels.
D. GonzálezJorge ÁlvarezJosé Antonio SánchezLeire GodinoIñigo PomboPublished in: Sensors (2022)
Keyphrases
- acoustic emission
- deep learning
- tool wear
- feature extraction
- condition monitoring
- unsupervised learning
- machine learning
- unsupervised feature learning
- mental models
- feature vectors
- fault detection
- weakly supervised
- fault diagnosis
- pattern recognition
- face recognition
- image classification
- multi sensor
- principal component analysis
- feature space
- multiscale
- feature selection