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Use of neural networks for sensor failure detection in a control system

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3 Author(s)
Naidu, S.R. ; Syst. Res. Center, Maryland Univ., College Park, MD, USA ; Zafiriou, E. ; McAvoy, Thomas J.

The use of the back-propagation neural network for sensor failure detection in process control systems is discussed. The back-propagation paradigm and traditional fault detection algorithms such as the finite integral squared-error method and the nearest-neighbor method are discussed. The algorithm is applied to the internal model control structure for a first-order linear time-invariant plant subject to high model uncertainty. Compared with traditional methods, the back-propagation technique is shown to be able to discern accurately the supercritical failures from their subcritical counterparts. The use of online adapted back-propagation fault detection systems in nonlinear plants is also investigated.<>

Published in:

Control Systems Magazine, IEEE  (Volume:10 ,  Issue: 3 )