Development of node-decoupled extended Kalman filter (NDEKF) training method to design neural network diagnostic/prognostic reasoners

Kenichi Kaneshige; Xudong Wang; Mark Saewong; Vassilis Syrmos

doi:10.1117/12.539317

21 July 2004 Development of node-decoupled extended Kalman filter (NDEKF) training method to design neural network diagnostic/prognostic reasoners

Kenichi Kaneshige, Xudong Wang, Mark Saewong, Vassilis Syrmos

Author Affiliations +

Proceedings Volume 5394, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems III; (2004) https://doi.org/10.1117/12.539317
Event: NDE for Health Monitoring and Diagnostics, 2004, San Diego, CA, United States

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.

Citation Download Citation

Kenichi Kaneshige, Xudong Wang, Mark Saewong, and Vassilis Syrmos "Development of node-decoupled extended Kalman filter (NDEKF) training method to design neural network diagnostic/prognostic reasoners", Proc. SPIE 5394, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems III, (21 July 2004); https://doi.org/10.1117/12.539317

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Neural networks

Filtering (signal processing)

Sensors

Evolutionary algorithms

Electronic filtering

Multilayers

Diagnostics

Show All Keywords

Keywords/Phrases

Search In:

Publication Years