Fault diagnosis and prognosis in discrete event systems using statistical model and neural networks
by M. Msaaf; F. Belmajdoub
International Journal of Mechatronics and Automation (IJMA), Vol. 6, No. 4, 2018

Abstract: This paper deals with fault diagnosis and fault prognosis in discrete event systems described by sequences of events. We are interested in large industrial systems where the modelling with tools usually used for discrete event system (automata, Petri net…) is a complex task. The description of DES is made with a statistical model composed of events recorded from the considered DES and regrouped in the form of temporal windows. In the first phase, the theoretical framework is developed to perform diagnosis and prognosis using statistical model and temporal window concept. The second phase uses the result of the first phase to train radial basic function neural networks that preform online diagnosis and online prognosis. A case study and several examples serving to illuminate the developed approach are given.

Online publication date: Mon, 08-Oct-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mechatronics and Automation (IJMA):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com