Diagnosability Design Method Based on Fault-sensitive Learning and Integrated Learning
The diagnosability design of system can optimize the diagnostic scheme and improve the the degree of compatibility between diagnostic scheme and system,which is of great significance for the accurate detection or isolation of system faults.For this,a diagnosability design method based on fault-sensitive learning and integrated learning is proposed.The featuresfrom the collected signals of system test points in different states are extracted.Combined with the results of diagnosability evaluation of the system,a quantitative feature contribution algorithm is proposed to evaluate the contribution degree of different features in the signal to fault diagnosis.The improved D-S evidence theory algorithm is used to fuse the features of different signals,which can determine the fault sensitive feature set suitable for fault detection and isolation.The integrated learning method is used to strengthen the diagnostic effect of base classifier,and finally the diagnosis scheme of the current system is obtained.Simulation experiments show that the system diagnosis scheme designed by the proposed diagnosability design method can make a good diagnosis of system faults.Compared with no fault-sensitive learning,the error rate of fault diagnosis decreases from 5.33％to 2.66％.Compared with no integrated learning,the error rate of fault diagnosis decreases from 16.22％(mean value of base diagnostics)to 2.66％.In the comparison experiments with other diagnostic schemes,the fault diagnosis error rate of the proposed method is decreased by 3.34％compared to the average fault diagnosis error rateof other methods.
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