Facility health maintenance through SVR-driven degradation prediction
by Xiangang Cao, Pingyu Jiang, Guanghui Zhou
International Journal of Materials and Product Technology (IJMPT), Vol. 33, No. 1/2, 2008

Abstract: In order to realise the health monitoring and maintenance of complex facilities with multiple degradation parameters, a facility synthetic failure probability model to map between inputs and probability of failure is established through adopting the logistic regression to synthesise each degradation parameter. Then, a SVR-driven degradation trend prediction and estimate of Remaining Useful Life (RUL) method is put forward. Last, based on Monte-Carlo method, a multi-parameters equipment emulator according with Weibull distribution is established to test the model. The results show that these methods are practicable.

Online publication date: Wed, 30-Jul-2008

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 Materials and Product Technology (IJMPT):
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