Helium blower test based on aerodynamic force simulation

Zhao, Jingjing; Sun, Zhe; Yan, Xunshi; Yang, Guang-Hong; Zhou, Yan; Liu, Xingnan; Shi, Zhengang; Fan, Tianpeng; Zhang, Xiaoshen

doi:10.1016/j.anucene.2018.04.017

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…To verify the performance of the proposed algorithm, we use a rig of rotating machinery supported by active magnetic bearing (AMB) for the experiments, and the AMB system is equipped with multiple displacement sensors to construct a fault dataset. AMB is a kind of mechatronic equipment that has the characteristics of no lubrication and no contact with rotor, and is widely used in various fields of industry, such as high-speed motors, compressors, vacuum pumps, etc [34][35][36]. The test rig is vertical with a rotor length of 1053 mm and a motor rotating speed of 6000 rpm.…”

Section: Methodsmentioning

confidence: 99%

Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments

Yan

Sun

Zhao

et al. 2019

Journal of Sound and Vibration

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Section: Methodsmentioning

confidence: 99%

Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments

Yan

Sun

Zhao

et al. 2019

Journal of Sound and Vibration

Self Cite

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“…AMB can also reduce the excessive vibration of a high-speed rotor system at the critical speed, which helps the motor run over the critical speed stably. These remarkable advantages enable the application of AMB to many fields, such as high speed motors, centrifugal compressors, high-speed flywheels for storage energy, molecular pumps and turbines [23,24,25].…”

Section: Active Magnetic Bearingmentioning

confidence: 99%

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

Yan

Sun

Zhao

et al. 2019

Sensors

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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.

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