期刊
MEASUREMENT
卷 152, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.107377
关键词
Fault diagnosis; Imbalanced data; Deep learning; Rotating machines; Generative adversarial networks
资金
- Fundamental Research Funds for the Central Universities [N180703018, N170503012, N180708009, N170308028]
- National Natural Science Foundation of China [11902202]
- Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education, Northeastern University [VCAME201906]
- Liaoning Provincial Department of Science and Technology [2019-BS-184]
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating machines, balanced training data for different machine health conditions are assumed in most studies. However, the signals in machine faulty states are usually difficult and expensive to collect, resulting in imbalanced training dataset in most cases. That significantly deteriorates the effectiveness of the existing data-driven approaches. This paper proposes a deep learning-based fault diagnosis method to address the imbalanced data problem by explicitly creating additional training data. Generative adversarial networks are firstly used to learn the mapping between the distributions of noise and real machinery temporal vibration data, and additional realistic fake samples can be generated to balance and further expand the available dataset afterwards. Through experiments on two rotating machinery datasets, it is validated that the data-driven methods can significantly benefit from the data augmentation, and the proposed method offers a promising tool on fault diagnosis with imbalanced training data. (C) 2019 Elsevier Ltd. All rights reserved.
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