NDLI: Fault diagnosis based on Grey-box Neural Network identification model

Content Provider	IEEE Xplore Digital Library
Author	Cen Zhaohui Wei Jiaolong Jiang Rui
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Information Engineering, Huazhong University of Science and technology, Wuhan, Hubei, China (Cen Zhaohui; Wei Jiaolong; Jiang Rui)
Abstract	This paper presents a fault diagnosis (FD) scheme for a class of nonlinear dynamic systems using a novel Grey-Box Neural Network Model (GBNNM). In this GBNNM, a composite structure, including MLP (multi-layer perception) NN (Neural Network) and integer term, is proposed to approximate both nonlinearity and dynamics of object system. Its approximation ability is then proved theoretically. And a self-defined exciting strategy is introduced into NN training to improve NN's generalization ability. Unlike previous NN model based fault diagnosis methods, a quantitative residual, which is obtained from system output and its GBNNM model output, can accurately indicates inconsistency caused by fault, so the improved residual is not essential for our scheme. The proposed FD scheme is applied in a high-fidelity Reaction Wheel (RW) in Satellite Attitude Control System (SACS) in our case study. The results of the case study demonstrate the effectiveness and superiority of our FD scheme.
Starting Page	249
Ending Page	254
File Size	822217
Page Count	6
File Format	PDF
ISBN	9781424474530
e-ISBN	9788993215021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-27
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	IEEE International Conference on Control & Automation
Subject Keyword	Training Fault diagnosis Grey-box Neural-network model (GBNNM) Wheels nonlinear dynamic systems Artificial neural networks Model Identification and Fault diagnosis Reaction wheel Nonlinear dynamical systems Approximation methods Mathematical model
Content Type	Text
Resource Type	Article

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