Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring
Chengming Shi(Huazhong University of Science and Technology), Fangyu Peng(Huazhong University of Science and Technology), Xinyong Mao(Huazhong University of Science and Technology), Hongqi Li(Northwestern Polytechnical University), Bin Li(Huazhong University of Science and Technology), Bo Luo(Huazhong University of Science and Technology)
Cited by 3
Related Papers
Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification
|IEEE Transactions on Industrial Electronics|2018|303
Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing
|IEEE Transactions on Industrial Electronics|2018|139
Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion
|IEEE Transactions on Industrial Informatics|2019|104
A novel adversarial domain adaptation transfer learning method for tool wear state prediction
|Knowledge-Based Systems|2022|66
Bayesian uncertainty quantification and propagation for prediction of milling stability lobe
|Mechanical Systems and Signal Processing|2019|56