@inproceedings{dc6d551a6cb246128384c40720683c09,
title = "An adversarial learning framework for zero-shot fault recognition of mechanical systems",
abstract = "Data imbalance is a major problem in intelligent fault diagnosis. Aiming at this problem, the paper proposed a novel adversarial learning framework for zero-shot fault recognition of mechanical systems. The proposed network consists of three parts which are the feature extractor, the generator and the discriminator. Trained with normal samples, the proposed method is capable of generating unseen fault samples by changing the condition of the generator. After, these synthetic samples are used to train an improved deep neural network for fault recognition. Results show that the proposed method can recognize the unseen faults even though none of fault samples are available during training, which is meaningful for industry application.",
keywords = "Data imbalance, Deep learning, Fault detection, Rolling bearing",
author = "Jinglong Chen and Tongyang Pan and Zitong Zhou and Shuilong He",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE International Conference on Industrial Informatics, INDIN 2019 ; Conference date: 22-07-2019 Through 25-07-2019",
year = "2019",
month = jul,
doi = "10.1109/INDIN41052.2019.8972316",
language = "英语",
series = "IEEE International Conference on Industrial Informatics (INDIN)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1275--1278",
booktitle = "Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019",
}