NDLI: Self-recovery method based on auto-associative neural network for intelligent sensors

Content Provider	IEEE Xplore Digital Library
Author	Huang Guo-jian Liu Gui-xiong Chen Geng-xin Chen Tie-qun
Copyright Year	2010
Description	Author affiliation: School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, 510640, China (Huang Guo-jian; Liu Gui-xiong; Chen Geng-xin; Chen Tie-qun)
Abstract	In order to improve the self-recovery capabilities of the IEEE 1451 based intelligent sensors and enhance the level of sensors' intelligence, this paper presents a sensors fault detection and repair method based on Auto-Associative Neural Network (AANN). The error sum of squares (SSE) is introduced as a sensor fault evaluation factor on the basis of the inherent non-linearity & non-orthogonal of the AANN, besides a parallel 9-ary tree algorithm is proposed to locate multi-faulty transducers. The 9-ary tree algorithm can be further extended to estimate the correct value of the faulty transducers while the SSE is less than threshold. A 10-13-5-13-10 structured AANN is constructed to test the self-recovery capability of an insulator contamination status online monitoring networked intelligent sensor model. Results show that, the AANN can be trained within 2 seconds. By altering the corrected step of the 9-ary tree algorithm successively; this method can locate at least two faulty transducers synchronously, besides it can take appropriate strategy for recovering the drift failures and estimating their real value within 5 seconds.
Starting Page	6918
Ending Page	6922
File Size	802359
Page Count	5
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554231
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transducers Intelligent sensors Artificial neural networks Training Noise Testing Auto-Associative Neural Network Intelligent Sensors Self-recovery 9-ary tree
Content Type	Text
Resource Type	Article

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