Application of improved ART-2A neural network in automatic fault detection and diagnosis

ART-2A Neural Network is effective in dealing with classification problem of pattern recognition,but it suffers from classification drift because of the shortage in algorithm structure,which may cause network instability,and in turn severely affect the engineering application of the network.The standard ART-2A network structure and algorithm are studied.And the causes of classification drift are analyzed.An improved ART-2A algorithm is presented and is used in Fault Detection and Diagnosis(FDD) experiments for verification of network stability and usability.The Experiments showed that the improved ART-2A algorithm can work steadily in a long term,and can distinguish fault correctly.The improved ART-2A network is helpful for realizing automatic FDD.