By Topic

The Information Fusion of Multi-sensor of Based on Federated Kalman Filter and Neural Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Bin Ling ; Inf. & Comput. Eng. Coll., Northeast Forest Univ., Harbin, China ; Xiaoyan Yu ; Lichen Liu

A new method of fault diagnosis is proposed. This method is called the complex fault diagnosis of based on federated kalman filter (FKF) and neural network (NN). It uses Kalman filter to estimate the measurable parameters' variations of car engine multi-sensor, and processes fault signal by information reconstruction, and then trains and corrects noise error by neural networks. According to these, it can diagnoses automobile engine fault. The simulation results show that the method is feasible and effective.

Published in:

Computational and Information Sciences (ICCIS), 2010 International Conference on

Date of Conference:

17-19 Dec. 2010