Advanced Growing Neural Network approach for the fault diagnosis of large scale industrial plants

Detecting and diagnosing faults before deteriorating the system performance is a crucial task for the reliability and safety of many engineering systems. A parameter selection with Self Adaptive Growing Neural Network (SAGNN) is developed for automatic Fault Detection and Diagnosis (FDD) in industrial environments. The growing and adaptive skill of SAGNN allows it to change its size and structure according to the training data. An advanced parameter selection criterion is embedded in SAGNN algorithm based on the computed performance rate of training samples. At growing stage, neurons are added to hidden subspaces of SAGNN while its competitive learning is an adaptive process in which neurons become more sensitive to different input patterns. The proposed classifier is applied to the fault detection and diagnoses of disturbances in chemical plant. Classification results are analyzed, explained and compared with various techniques based on neural networks.