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Proceedings Paper

Blockage fault diagnosis method of combine harvester based on BPNN and DS evidence theory
Author(s): Jin Chen; Kai Xu; Yifan Wang; Kun Wang; Shuqing Wang
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Paper Abstract

According to the complexity and the lack of intelligent analysis method of combine harvester blockage fault , this paper puts forward a method , based on the combination of BP neural network (BPNN)and DS evidence theory , for combine harvester blockage fault diagnosis. Choosing cutting table auger, conveyer trough, threshing cylinder and grain conveying auger as the study, this paper divides the condition of combine harvester into four categories, namely, normal, slightly blocking, blockage, severe blockage, which being as an identification framework for DS evidence theory. BP neural network is used for analysing speed information of monitoring points and distributing basic probability for each proposition in the identification framework. Dempster combination rule converged information at different time to obtain diagnostic results.Test results show that this method can timely and accurately judge the work state of combine harvester, the blocking fault warning time will be increased to 2 seconds and the success probability of blocking fault warning reach more than 90%.

Paper Details

Date Published: 23 January 2017
PDF: 9 pages
Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103224C (23 January 2017); doi: 10.1117/12.2265524
Show Author Affiliations
Jin Chen, Jiangsu Univ. (China)
Kai Xu, Jiangsu Univ. (China)
Yifan Wang, Jiangsu Univ. (China)
Kun Wang, Jiangsu Univ. (China)
Shuqing Wang, Jiangsu Univ. (China)


Published in SPIE Proceedings Vol. 10322:
Seventh International Conference on Electronics and Information Engineering
Xiyuan Chen, Editor(s)

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