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Using neural network to diagnose the faults may occur the problems such as difficult selection of input vector, complex structure of network and ineffective for simultaneous fault diagnosis. For that reason, this paper proposes a fault diagnosis method based on neighborhood rough sets and parallel neural networks. We first use neighborhood rough sets to reduce the initial signs. The remaining valuable signs will be used as the input vector of neural network. Then we design neural networks for each type of fault. We use the trained neural networks to diagnose the faults in parallel and give the final diagnosis conclusion according to the results of each network. We have tested the method by using the experimental data of rotor test stand and found that this method can optimize the structure of neural network and the networks need less training time and can ensure the accuracy of fault diagnosis.