Cost-sensitive Fault Diagnosis Based on Weighted Rough Sets
Wang Wei · Proceedings of the CSEE · 2007
In the realm of fault diagnosis,rough sets have been a powerful tool to deal with the inconsistent information.However,when the misdiagnosis costs of different faults are unequal,classical rough sets can not acquire a satisfying result due to the absence of a mechanism considering the apriori knowledge.Through the introduction of subjective weights on data,this paper proposed a weighted rough set learning method to consider the apriori knowledge in rough sets,where an algorithm of weighted attribute reduction and an algorithm of weighted rule extraction were designed respectively.Based on weighted rough sets,the method of cost-sensitive fault diagnosis was provided and experiments on the cost-sensitive fault diagnosis of vibration faults of steam turbine was carried out.The results show that in the weighted rough set based cost-sensitive fault diagnosis,the key symptoms of high-cost faults are selected preferentially,and the bigger factors of support and confidence are obtained for the rules of high-cost faults in the generated rules.When the outputs of fault diagnosis are inconsistent,the weighted rough set based cost-sensitive fault diagnosis is inclined to output the high-cost fault and decreases the overall costs of fault diagnosis.