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A Fault Diagnosis Method Based on Artificial Immune Network

A new fault diagnosis method based on artificial immune network is proposed. The network combined aiNet with radial basis function (RBF) NN. The structure of the network proposed is the same as RBF NN. The training samples are clustered first by the improved aiNet algorithm. The centers of the clustering are saved as the centers of the hidden layer, therefore, the amount and positions of nodes in the hidden layer can be determined automatically. The weight matrix is determined by least squares (LS) algorithm. The network is applied to fault diagnosis of CJK6136 spindle gear case. The results of the experiments confirm the performance of the proposed network through comparing with RBF NN under the same conditions. The diagnosis success rate for the network proposed was 99%, while that for RBF NN is 89.5%.