Fault Diagnosis of Marine Diesel Engine by Means of Immune-Rough Sets and RBF Neural Network

摘要

A new hybrid intelligent model of rough sets and RBF neural networks for fault diagnosis is proposed. Meanwhile, a novel attribute reduction approach of rough set based on artificial immune algorithm is proposed, that can find several different minimal feature set of decision table through clonal selection, mutation and antibody suppressing strategy, then provide more selection for fault diagnosis. The diagnosis of large marine diesel engine showed that the model can reduce the cost of diagnosis and increase the efficiency of diagnosis. There will be well application prospect in practice.