On fault isolation by neural-networks-based parameter estimation techniques

Abstract: The aim of the work is to exploit some aspects of functional approximation techniques in parameter estimation procedures applied on fault detection and isolation tasks using backpropagation neural networks as functional approximation devices. The major focus of this paper deals with the strategy used in the data selection task as applied to the determination of non-conventional process parameters, such as performance or process-efficiency indexes, which are difficult to acquire by direct measurement. The implementation and validation procedure on a real case study is carried out with the aid of the facilities supplied by commercial neural networks toolboxes, which manage databases, neural network structures and highly efficient training algorithms.