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RISS 인기검색어
- 검색키워드
- 저자 : Mingzhuang Wu (검색결과 2 건)
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A novel vibration isolator for vibrating screen based on magnetorheological damper
Mingzhuang Wu,Fei Chen,Aimin Li,Ziye Chen,Nana Sun 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.10
A vibrating screen is widely used in raw coal screening, but intensive resonance in the startup and shutdown stages shortens the service life of the vibrating screen and generates vibration damage to surrounding buildings. Therefore, we designed a novel vibration isolator based on a magnetorheological damper, aiming to improve the vibration isolation performance of the vibrating screen. The rheological mechanical model of damping force was analyzed based on the Bingham model, and a magnetic circuit was designed according to electromagnetic theory. Then, an experiment was designed to evaluate the vibration isolation performance of the vibration isolator. The results show that the novel vibration isolator, compared with the metal spring ones, reduces the maximum resonance amplitude by 64 % in the resonance region. In addition, the time of passing through the resonance region in startup and shutdown stages is also reduced by 50 % and 60 %, respectively. This study can provide a new method to improve the vibration isolation performance of vibrating screens.
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Fault diagnosis method of belt conveyor idler based on sound signal
Yahui Zhang,Siyan Li,Aimin Li,Gaoxiang Zhang,Mingzhuang Wu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.1
Damage to a belt conveyor idler will increase the downtime and maintenance cost, so it is very important to diagnose its fault. At present, the fault diagnosis of the idler of a belt conveyor is mostly based on vibration and temperature signal. However, contact fault diagnosis approaches are severely limited when sensors are inconvenient to install or when vibration and temperature signals cannot be returned. In this special case, the non-contact fault diagnosis method, represented by measuring acoustic signals, becomes a necessary means. To effectively extract mechanical state information from sound signals of belt conveyors and identify typical mechanical faults, we propose a fault detection method based on sample center distance weighted (support vector data description (SVDD)) and multi-frame fusion (Melfrequency cepstral coefficient (MFCC)) features. Aiming at the disadvantage that single frame MFCC features and traditional SVDD are susceptible to noise, multi-frame fusion MFCC optimization features are used as samples, and the weighted SVDD model based on sample center distance is used for fault detection. Finally, the overall recognition accuracy of the experiment is greatly improved. It is proved that MFCC features of multi-frame fusion sound signal and weighted SVDD fault detection based on sample center distance can effectively determine whether there is a fault in the of belt conveyor idler.
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