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

Abstract

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.