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| Fault Monitoring and Diagnosis Method of Rare Earth Extraction Transmission Device Based on Rough Set Theory and BP Neural Networks |
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Received:September 04, 2016
Revised:September 08, 2016
Accepted:September 09, 2016
Published Online:October 21, 2016
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| DOI： |
| KeyWord:BP Neural Networks; Extraction of rare earth; Transmission; Fault diagnosis |
| Author | Institution |
| huzhenguang |
School of Geoscinces and Info-Physics Central South University,Chinalco Gxnf Rareearth Development Co.,LTD |
| chen songling |
School of Geoscinces and Info-Physics Central South University |
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| Abstract: |
| In the production line of rare earth production extraction process, if the extraction agitator has failed to stop stirring, it not only will affect the quality of production, but also would cause the extraction tank to spill over to affect the entire production line. The agitator is driven by the motor through the driving device to complete the stirring action, so the normal operation of the transmission device is very important for the rare earth extraction process. After the failure of the transmission device, when the fault information is incomplete or inconsistent, it is difficult to get the correct conclusion for fault diagnosis. Aiming at this problem, A kind of fault monitoring device for rare earth extraction drive device was designed. A fault diagnosis method based on rough set and BP neural network is proposed. Finally, it is proved that the fault diagnosis method has a faster convergence rate, and the accurate location of the fault diagnosis. |
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