Knowledge acquisition for aero-engine wear fault diagnosis based on rule extraction from neural networks

In view of the problem that it is difficult to understand the knowledge and diagnosis process in intelligent and expert systems based on neural network,a new rule extraction method from neural network based on the functional point of view was studied,and the flow and the key algorithms of the new method were introduced.The UCI(University of California Irvine)machine learning data were used to analyze and verify the rule extraction method.Finally,this method was applied to aero-engine wear faults diagnosis.237 spectral oil analysis samples were acquired from practical aero-engine,the rules extraction from NN(Neural netwoks) method was used to extract the diagnosis knowledge rules,the extracted rules were explained and analyzed.The results fully show the correctness and rationality of the new method.