Fault diagnosis of pneumatic valve using PCA and ANN techniques

Abstract

Detection and Diagnosis of faults in pneumatic valve used in cooler water spray system in cement industry is of great practical significance and paramount importance for the safe operation of the plant. In this paper the dimensionality reduction techniques such as principal component analysis (PCA) are used to reduce the input features is proposed. PCA is used to extract the primary features associated with the pneumatic valve used in cooler water spray system. The training and testing data required for the dimensionality reduction technique such as PCA model were created at normal and faulty conditions of pneumatic valve in a real time laboratory experimental setup. The performance of the developed PCA model is compared with the MLFFNN (Multilayer Feed Forward Neural Network) trained by the back propagation algorithm. From the simulation results it is observed that the performance of PCA had the best classification properties when it is compared with the performance of ANN. © 2011 Springer-Verlag Berlin Heidelberg.