|Xunshi yan; Chen-an zhang; Zhengang shi; Jingjing zhao; Zhe sun; Zhang CA(张陈安)|
|Keyword||Fault diagnosis Vibration signals Active magnetic bearing Rotary machinery Adaboost|
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.
|Corresponding Author||Xunshi yan; Chen-an zhang; Zhang CA(张陈安)|
|Affiliation||1.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
|Xunshi Yan,Chen-An Zhang,Zhengang Shi,et al. Fault diagnosis of active magnetic bearing–rotor system via vibration images[J]. Sensors,2019,19(2):244.|
|APA||Xunshi Yan,Chen-An Zhang,Zhengang Shi,Jingjing Zhao,Zhe Sun,&张陈安.(2019).Fault diagnosis of active magnetic bearing–rotor system via vibration images.Sensors,19(2),244.|
|MLA||Xunshi Yan,et al."Fault diagnosis of active magnetic bearing–rotor system via vibration images".Sensors 19.2(2019):244.|
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