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Proceedings Paper

TWT transmitter fault prediction based on ANFIS
Author(s): Mengyan Li; Junshan Li; Shuangshuang Li; Wenqing Wang; Fen Li
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Paper Abstract

Fault prediction is an important component of health management, and plays an important role in the reliability guarantee of complex electronic equipments. Transmitter is a unit with high failure rate. The cathode performance of TWT is a common fault of transmitter. In this dissertation, a model based on a set of key parameters of TWT is proposed. By choosing proper parameters and applying adaptive neural network training model, this method, combined with analytic hierarchy process (AHP), has a certain reference value for the overall health judgment of TWT transmitters.

Paper Details

Date Published: 15 November 2017
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060547 (15 November 2017); doi: 10.1117/12.2296313
Show Author Affiliations
Mengyan Li, Shanghai Radio Equipment Research Institute (China)
Junshan Li, Shanghai Radio Equipment Research Institute (China)
Shuangshuang Li, Shanghai Radio Equipment Research Institute (China)
Wenqing Wang, Shanghai Radio Equipment Research Institute (China)
Fen Li, Shanghai Radio Equipment Research Institute (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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