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Neural Network fault Classifier for Sensor fault Diagnosis of an Industrial System using Integral absolute error criterion

V. Manikandan*, K. Ramakrishnan**, N. Devarajan***, D. Sreekanth****

*Senior Lecturer, EEE Dept, Coimbatore Institute of Technology, Coimbatore.

**Senior Lecturer, EEE Dept, Pondichery engineering college , Pondichery

***Assistant Professor, EEE Dept., Governmant College of Technology, Coimbatore.

****PG Student, Dept of EEE, Coimbatore Institute of Technology, Coimbatore.

Periodicity:May - July'2006
DOI : https://doi.org/10.26634/jfet.1.4.991

Abstract

The scope of this paper is to present a neural network approach towards sensor fault (deterioration) diagnosis in Linear Time Invariant(LTI) systems. The novelty of the approach lies in associating with each state feedback gain factor a scalar a, which is defined as the sensor healthiness factor. This scalar is made to vary from 1(no fault condition) to 0(full fault condition) in predetermined steps. The intermediate values of a portray the deterioration modes of the sensor. The Integral Absolute Error (IAE) criterion is employed for extracting the signature of the fault and the classification is done using Artificial Neural Network (ANN) classifier. The proposed diagnosis approach is applied to a dc motor system to validate the effectiveness of the technique.

Keywords

Diagnosis, State Feedback, ANN

How to Cite this Article?

V. Manikandan, K. Ramakrishnan, N. Devarajan and D. Sreekanth (2006). Neural Network fault Classifier for Sensor fault Diagnosis of an Industrial System using Integral absolute error criterion. i-manager’s Journal on Future Engineering and Technology, 1(4), 77-83. https://doi.org/10.26634/jfet.1.4.991

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Neural Network fault Classifier for Sensor fault Diagnosis of an Industrial System using Integral absolute error criterion

Abstract

Keywords

How to Cite this Article?

References

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