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Proceedings Paper

Fault diagnosis in LED illuminating circuits based on cloud model
Author(s): Qi Liu; Hong-Dong Zhao; Jie Zhao
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Paper Abstract

For gaining effective features to realize fault diagnosis in the LED illuminating circuits, a method of fault diagnosis in analog circuits based on cloud model is proposed. In this paper, the analog circuit with a sinusoidal input is simulated and its output is sampled to extract sequences of each layer of wavelet coefficients as the initial fault feature vectors. Then, the backward cloud algorithm of cloud model is applied to obtain corresponding digital features of the wavelet coefficients, which include the Expected value Ex, the Entropy En and the Hyper entropy He as new fault feature vectors, named fault cloud feature vectors. Finally, Fault cloud feature vectors are used to BP neural networks to classify fault and realize the fault diagnosis in analog circuits. The simulation result on the LED illuminating circuits shows that this method is feasible and has many powerful virtues, such as diagnosing and locating faults quickly and exactly.

Paper Details

Date Published: 15 November 2010
PDF: 6 pages
Proc. SPIE 7852, LED and Display Technologies, 78520T (15 November 2010); doi: 10.1117/12.868885
Show Author Affiliations
Qi Liu, Hebei Univ. of Technology (China)
Tianjin Institute of Urban Construction (China)
Hong-Dong Zhao, Hebei Univ. of Technology (China)
Jie Zhao, Tianjin Institute of Urban Construction (China)

Published in SPIE Proceedings Vol. 7852:
LED and Display Technologies
Gang Yu; Yanbing Hou, Editor(s)

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