Application of one-class support vector machines to fault analysis of turbopump test data

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School of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073, China [1 ]
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Zhendong Ceshi Yu Zhenduan | 2007年 / 2卷 / 95-97期
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To deal with the lack of fault samples in the fault detection of a Liquid Rocket Engine (LRE) turbopump, a detection model based on one-class support vector machines (OC-SVMs) was founded. As a novel detector, the model requires no negative training instances and is able to solve classification tasks on the basis of positive samples only. And a multi-layer high speed training strategy was proposed for the improvement of training efficiency. The analysis of LRE historical test data showed that the algorithm has a high training efficiency and can detect the faults of the LRE turbopump.
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