Fault detection based on neural networks and independent component analysis

In this paper a method that integrates neural networks (NN) and independent component analysis (ICA) is used to detect faults in non-linear plants. The neural networks are used to calculate a non-linear and dynamic model of the process in normal operation conditions. ICA is used to monitor and to detect faults in the process using, instead of the measured variables of the process, the residuals calculated as the difference between the process measurements and the output of the networks. This technique has been applied in simulation to a benchmark of the biological wastewater treatment process, a highly non-linear process. In order to prove the advantages of using this monitoring technique called NNICA, a comparison with the classical PCA and ICA methods is carried out in the paper.