An input-training neural network based nonlinear principal component analysis approach for fault diagnosis

In this paper some existing problems in the linear principal component analysis methodology are discussed first. A nonlinear principal component analysis methodology based upon input-training neural network is presented for process fault diagnosis. The learning algorithm of input-training neural network is modified to improve its learning speed and avoid oscillation during learning. Then, input-training neural network and BP neural network are used to estimate the nonlinear principal component scores. Fault detection and diagnosis is performed by means of statistical methods like Hotelling´s T² and Q. Finally, the simulation research to continuous stirred tank reactor is performed to show its advantages in extracting the nonlinear features compared with the linear principal component analysis methodology.