Abstract
As fault classification becomes more important in manufacturing industry, the state-of-art machine learning methods have been utilized. However, owing to the problem called catastrophic forgetting, the networks tend to forget the former knowledge. Thus, it is evident that overall classification performance has fallen, when training the existing model with new classes. We propose classification model that retains previous information using generative pseudorehearsal networks. In this method, newly arrived fault classes could be trained on same network which is parameterized by former data. The proposed method shows significant experimental results comparing to non-incremental methods, while achieving memory efficiency and solving the class imbalance problem.
Original language | English |
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Title of host publication | Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 |
Editors | Meikang Qiu |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 138-140 |
Number of pages | 3 |
ISBN (Electronic) | 9781728116631 |
DOIs | |
Publication status | Published - 2019 Aug |
Event | 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 - New York, United States Duration: 2019 Aug 1 → 2019 Aug 3 |
Publication series
Name | Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 |
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Conference
Conference | 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 |
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Country/Territory | United States |
City | New York |
Period | 19/8/1 → 19/8/3 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Fault diagnostics and classification
- Generative networks
- Incremental learning
- Pseudorehearsal strategy
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Human-Computer Interaction
- Artificial Intelligence