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Proceedings Paper

Development of node-decoupled extended Kalman filter (NDEKF) training method to design neural network diagnostic/prognostic reasoners
Author(s): Kenichi Kaneshige; Xudong Wang; Mark Saewong; Vassilis Syrmos
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Paper Abstract

In this paper, we have proposed diagnostic techniques using a multilayered neural network where the weights in the network are updated using node-decoupled extended Kalman filter (NDEKF) training method. Sensor signals in both time domain and frequency domain are analyzed to show the effectiveness of the NDEKF algorithm in each domain. Comparisons of the NDEKF algorithm with other popular neural network training algorithms such as extended Kalman filter (EKF) and backpropagation (BP) will be discussed in the paper through a system identification problem. First, the simulation results reveal the comparison of outputs from actual system and trained neural network. Secondly, the ability of diagnosing a system with one normal condition and three known fault conditions is demonstrated. Thirdly, the robustness of the machine condition monitoring when the inputs to the system vary is shown. The proposed technique works even when there is noise in sensor signals as well.

Paper Details

Date Published: 21 July 2004
PDF: 10 pages
Proc. SPIE 5394, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems III, (21 July 2004); doi: 10.1117/12.539317
Show Author Affiliations
Kenichi Kaneshige, Univ. of Hawaii/Manoa (United States)
Xudong Wang, Univ. of Hawaii/Manoa (United States)
Mark Saewong, Univ. of Hawaii/Manoa (United States)
Vassilis Syrmos, Univ. of Hawaii/Manoa (United States)


Published in SPIE Proceedings Vol. 5394:
Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems III
Tribikram Kundu, Editor(s)

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