21 July 2004 Development of node-decoupled extended Kalman filter (NDEKF) training method to design neural network diagnostic/prognostic reasoners
Author Affiliations +
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.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenichi Kaneshige, Xudong Wang, Mark Saewong, 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); doi: 10.1117/12.539317; http://dx.doi.org/10.1117/12.539317
PROCEEDINGS
10 PAGES


SHARE
KEYWORDS
Neural networks

Filtering (signal processing)

Sensors

Evolutionary algorithms

Electronic filtering

Multilayers

Diagnostics

RELATED CONTENT


Back to Top