Artificial neural network based decision-making fault diagnosis in a normalised DC motor system
by V. Manikandan, N. Devarajan
International Journal of Automation and Control (IJAAC), Vol. 3, No. 2/3, 2009

Abstract: The aim of this write up is to deal with the identification of parameter deviations using Artificial Neural Networks (ANNs). The diagnostic approach is accomplished in two steps: In Step 1, the system is identified using a series of input/output variables through an identification algorithm. In Step 2, the fault is diagnosed comparing the Markov parameters of faulty and non-faulty systems. The ANN is trained using predetermined faulty conditions serves to classify the unknown fault. In Step 1, the identification is done by first formulating a Hankel matrix out of input/output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online, sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out online. Fault residues are extracted making a comparison of the first five Markov parameters of the faulty and non-faulty systems. The proposed diagnostic approach is illustrated on a normalised DC motor system with encouraging results.

Online publication date: Sun, 17-May-2009

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 Automation and Control (IJAAC):
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