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《Journal of Huazhong University of Science and Technology(Nature Science Edition)》 2007-07
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Information entropy fusion in fault diagnosis of steam turbine shaft systems

Zhang Yanping Huang Shuhong Gao Wei Wang Kun (College of Energy and Power Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)  
Four information entropy samples,singular spectrum entropy,power spectrum one,wavelet energy spectrum one and wavelet space state spectrum one were calculated as information entropy data,after the faulty signals were collected from the rotor test rig.Probability neural networks(PNN) was explored to fuse the four information entropy samples.Research shows that with the advantages of Bayes classifier and neural networks,PNN have good classification ability of typical vibration faults of turbine,the classification accuracy is 100 % for training data,80 % for fresh data.Compared with the classification accuracy of the improved minimum distance classifier,PNN have more high solutions,which is a practical fusion diagnosis method for typical fault identification of turbine rotors.
【Fund】: 国家自然科学基金资助项目(50505013)
【CateGory Index】: TK268
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