A Simple Framework to Generalized Zero-Shot Learning for Fault Diagnosis of Industrial Processes
Author:
Affiliation:
1. School of Engineering, Huzhou University,Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems,Huzhou,China,313000
2. School of Intelligent Manufacturing, Huzhou College,Huzhou,China,313000
Funder
National Natural Science Foundation of China
Zhejiang Provincial Public Welfare Technology Application Research Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Information Systems,Control and Systems Engineering,Control and Optimization
Link
http://xplorestaging.ieee.org/ielx7/6570654/10141540/10084417.pdf?arnumber=10084417
Reference11 articles.
1. A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults
2. Deep Variational Autoencoder Classifier for Intelligent Fault Diagnosis Adaptive to Unseen Fault Categories
3. Fault Classification of Industrial Processes based on Generalized Zero-Shot Learning
4. Hybrid attribute conditional adversarial denoising autoencoder for zero-shot classification of mechanical intelligent fault diagnosis
5. An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
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1. Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes;IEEE/CAA Journal of Automatica Sinica;2024-01
2. A review of bearing fault diagnosis based on weakly supervised learning;2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS);2023-09-22
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