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Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
Yan XS; Sun Z; Zhao JJ; Shi ZG; 张陈安
Source PublicationSensors

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.

KeywordFault Diagnosis Vibration Signals Active Magnetic Bearing Rotary Machinery Adaboost
Indexed BySCI ; EI
WOS IDWOS:000458569300027
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Document Type期刊论文
Corresponding AuthorYan XS; 张陈安
Affiliation1.Institute of Nuclear and New Energy Technology, Tsinghua University
2.State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yan XS,Sun Z,Zhao JJ,等. Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images[J]. Sensors,2019,19(2):244.
APA Yan XS,Sun Z,Zhao JJ,Shi ZG,&张陈安.(2019).Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images.Sensors,19(2),244.
MLA Yan XS,et al."Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images".Sensors 19.2(2019):244.
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