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Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images

1,2,3,*, 1,2,3, 1,2,3, 1,2,3 and 4,*
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China
Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China
State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
Authors to whom correspondence should be addressed.
Sensors 2019, 19(2), 244;
Received: 10 December 2018 / Revised: 5 January 2019 / Accepted: 5 January 2019 / Published: 10 January 2019
(This article belongs to the Section State-of-the-Art Sensors Technologies)
PDF [2442 KB, uploaded 10 January 2019]


As important sources in fault diagnosis of rotary machinery, vibration signals are usually processed in the time or frequency domain as features to distinguish different classes of faults. However, these kinds of processing methods always ignore the corresponding relations among multiple signals, resulting in information loss. In this paper, a new fault description strategy named vibration image is proposed, based on which three new kinds of features are extracted, containing coupling information between different channels of vibration signals. Additionally, a new feature fusion method called two-layer AdaBoost is designed to train the fault recognition model, which avoids overfitting when the dataset is not large enough. Features based on vibration images combined with two-layer AdaBoost are adopted to diagnose faults of rotary machinery. Taking an active magnetic bearing-rotor system as the experimental platform, a dataset with four classes of faults is collected and our algorithm achieves good performance. Meanwhile, features based on vibration images and two-layer AdaBoost are both proved to be efficient separately. View Full-Text
Keywords: fault diagnosis; vibration signals; active magnetic bearing; rotary machinery; AdaBoost fault diagnosis; vibration signals; active magnetic bearing; rotary machinery; AdaBoost

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Yan, X.; Sun, Z.; Zhao, J.; Shi, Z.; Zhang, C.-A. Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images. Sensors 2019, 19, 244.

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