By Topic

Computational intelligence based machine fault diagnosis

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

4 Author(s)
Wang, D.D. ; Fac. of Mech. Eng., Beijing Univ. of Sci. & Technol., China ; Debing Yang ; Jinwu Xu ; Ke Xu

Machine fault diagnosis is a well established area where specific techniques are used to determine fault patterns or locations. In recent years, there are many studies about this issue by means of model based approach, probabilistic method, knowledge based approach and neural networks based approach et al. With the progress of the study of biology, evolutionary thought has extended into engineering problem-solving. More interests have been shown in this field. The investigation will describe two unsupervised clustering paradigms, Kohonen's self-organizing scheme and genetic algorithm (GA) based heuristic searching, for machine fault classification. In case study, a multiple faults classification problem has been attacked. Solutions generated from the GA based system are compared with that from self-organization neural networks, and the result is given, and the case study has shown that the proposed approaches are flexible enough to be used in practical fault diagnosis

Published in:

Industrial Technology, 1996. (ICIT '96), Proceedings of The IEEE International Conference on

Date of Conference:

2-6 Dec 1996