Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

Automatic generation method of optimum symptom parameters for condition diagnosis of plant machinery by genetic algorithms

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

2 Author(s)
Peng Chen ; Fac. of Comput. Sci. & Syst. Eng., Kyushu Inst. of Technol., Fukuoka, Japan ; Toyota, T.

When using computers for the automatic condition diagnosis of plant machinery, symptom parameters (SP) extracted from some signals are indispensable. Currently, however there is no acceptable method for attracting the optimum SP. In order to overcome this difficulty and ensure highly accurate condition diagnosis, a new method called the “automatic generation of symptom parameters” is proposed by using genetic algorithms (GA). The authors have applied the method to many diagnoses of plant machinery and, in each case, the optimum SP has been quickly discovered. In this paper, they show the example of gear equipment diagnosis to verify the efficiency of this method

Published in:

Environmentally Conscious Design and Inverse Manufacturing, 1999. Proceedings. EcoDesign '99: First International Symposium On

Date of Conference:

1-3 Feb 1999