DIAGNOSIS OF MULTIPLE SIMULTANEOUS FAULT VIA HIERARCHICAL ARTIFICIAL NEURAL NETWORKS

We discuss a new type of macroarchitecture of neural networks called a HANN and how to train it for fault diagnosis given appropriate data p atterns. The HANN divides a large number of patterns into many smaller subsets so the classification can be carried out more efficiently via an artificial neural network. One of its advantages is that multiple faults can be detected in new data even if the network is trained with data representing single faults. The use of a HANN is illustrated in fault diagnosis of a chemical reactor.