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《南京航空航天大学学报：英文版》
>APPLICATION OF ROUGH SET THEORY TO MAINTENANCE LEVEL DECISION-MAKING FOR AERO-ENGINE MODULES BASED ON INCREMENTAL KNOWLEDGE LEARNING
APPLICATION OF ROUGH SET THEORY TO MAINTENANCE LEVEL DECISION-MAKING FOR AERO-ENGINE MODULES BASED ON INCREMENTAL KNOWLEDGE LEARNING
The maintenance of an aero-engine usually includes three levels,and the maintenance cost and period greatly differ depending on the different maintenance levels.To plan a reasonable maintenance budget program, airlines would like to predict the maintenance level of aero-engine before repairing in terms of performance parameters,which can provide more economic benefits.The maintenance level decision rules are mined using the historical maintenance data of a civil aero-engine based on the rough set theory,and a variety of possible models of updating rules produced by newly increased maintenance cases added to the historical maintenance case database are investigated by the means of incremental machine learning.The continuously updated rules can provide reasonable guidance suggestions for engineers and decision support for planning a maintenance budget program before repairing. The results of an example show that the decision rules become more typical and robust,and they are more accurate to predict the maintenance level of an aero-engine module as the maintenance data increase,which illustrates the feasibility of the represented method.
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