TY - GEN
T1 - Generative pseudorehearsal strategy for fault classification under an incremental learning
AU - Lee, Subin
AU - Baek, Jun Geol
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C2005949). This work was also supported by the BK21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University) and by the Samsung Electronics Co., Ltd.
Funding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C2005949). This work was also supported by the BK21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University) and by the Samsung Electronics Co., Ltd.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - Fault diagnostics and classification
KW - Generative networks
KW - Incremental learning
KW - Pseudorehearsal strategy
UR - http://www.scopus.com/inward/record.url?scp=85077037210&partnerID=8YFLogxK
U2 - 10.1109/CSE/EUC.2019.00035
DO - 10.1109/CSE/EUC.2019.00035
M3 - Conference contribution
AN - SCOPUS:85077037210
T3 - Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
SP - 138
EP - 140
BT - Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
A2 - Qiu, Meikang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
Y2 - 1 August 2019 through 3 August 2019
ER -