Adaptive Knowledge Distillation-Based Lightweight Intelligent Fault Diagnosis Framework in IoT Edge Computing
Author:
Affiliation:
1. the School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, China
2. School of Automation, China University of Geosciences, Wuhan, China
Funder
National Key Research and Development Program of China Young Scientists Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6488907/10570342/10496469.pdf?arnumber=10496469
Reference45 articles.
1. Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
2. Convolutional Neural Network-based Bayesian Gaussian Mixture for Intelligent Fault Diagnosis of Rotating Machinery
3. Intelligent Diagnosis Using Continuous Wavelet Transform and Gauss Convolutional Deep Belief Network
4. Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings
5. Deep residual shrinkage relation network for anomaly detection of rotating machines
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