Detection and diagnosis of faults in cement industry is of great practical significance and paramount importance for the safe operation of the plant. In this paper, the design and development of Adaptive Neuro-Fuzzy Inference System (ANFIS) based fault detection and diagnosis of pneumatic valve used in cooler water spray system in cement industry is discussed. The ANFIS model is used to detect and diagnose the occurrence of various faults in pneumatic valve used in the cooler water spray system. The training and testing data required for model development were generated at normal and faulty conditions of pneumatic valve in a real time laboratory experimental setup. The performance of the developed ANFIS 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 ANFIS performed better than ANN. 
Fault detection and diagnosis of pneumatic valve using Adaptive Neuro-Fuzzy Inference System approach