A fault detection method based on radial basis function (RBF) immune neural network is proposed. The fault detection method consists of functional modules such as system identification, residual filtering and fault alarm concentration. System identification is based on immune RBF neural network. The residual used for fault detection is obtained by online comparison between the model output of the system and the actual output of the system. The generalization ability interference factor is introduced into the affinity function of the clonal selection algorithm to enhance the generalization ability of the RBF network. In this fault detection method, by filtering the residual and introducing the fault alarm concentration, the fault detection is only sensitive to the residual caused by the fault. The fault detection example of the parallel robot shows that the method can effectively detect and locate the driver fault and sensor fault, and has good noise tolerance performance.
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