A New Hybrid Intelligent Fault Diagnosis Model for Steamer
Xu Zhang, Chen Guang Guo, Jianbo Sun · 2006
Considering the ability of rough sets theory on reduction of decision system and that of neural networks for clustering and nonlinear mapping, a new hybrid intelligent model of rough sets and neural networks for fault diagnosis is proposed. Meanwhile, a novel attribute reduction approach of rough set based on immune clonal selection is proposed, in order to find the minimal feature set of decision table. Then, RBF neural network was designed to diagnose the faults occurred in steamer axes vibration, in which the results of attribute reduction are regarded as the input nodes and the decision attributes are regarded as the output nodes correspondingly. The experimental results showed that the model can reduce the cost of diagnosis and increase the efficiency of diagnosis. There will be well application prospect in practice.