A Master-Slave Neural Network for Precise Diagnosis of Large Rotating Machinery

As a good classifier, BP neural network has been applied in many engineering research questions. However, because of some inherent shortages, especially chaotic behaviors in the network learning, it is very difficult or impossible to apply the artificial neural network into precise diagnosis of large rotating machinery, such as the diagnosis of unbalance fault, etc. Based on good properties of the Hopfield neural network, a new master-slave neural network model (simply denoted as MSNN) is presented in this paper firstly, whose master network is two Hopfield networks, and the other slave network is a BP network, respectively. After its structure had been innovatively designed, the training algorithm of the MSNN was also discussed simply. At last, the new neural network is applied in the fault diagnosis of some large rotating machinery. By application analyzes and compares, the results show that the master-slave neural network includes more advantages than the BP network, such as a quick asymptotic convergence rate and the smallest network system errors. So, it can successfully be applied in precise diagnosis of large rotating machinery other than BP network.