Zhe Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zhe Sun has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Chen-An Zhang
State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
1 shared publication - Jingjing Zhao
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China, The Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China
1 shared publication - Xunshi Yan
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China
1 shared publication - Zhengang Shi
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China
1 shared publication
Latest Publications
- Open Access
Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
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.
Open AccessFault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
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.