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- W2368794739 abstract "The accurate identification of the fault conditions of steam turbine rotor has been the research focus in the field of engineering. In the process of fault diagnosis by using support vector machine( SVM),extracting the signal characteristic parameters,which can clearly distinguish different fault signals to construct high-quality samples,plays a significant role in improving the classification accuracy of SVM model. To solve these problems,we propose a multiple fault diagnosis method for steam turbine rotor based on ensemble empirical mode decomposition( EEM D),permutation entropy and SVM. Firstly,this method applies directed acyclic graph to establish multiple faults diagnosis model,and uses EEM D to decompose the vibration signals into single and unmixed IM F components. Then,the permutation entropy of IM F component,which is very sensitive to the changes in vibration signal,is calculated as eigenvectors,and applied in directed acyclic graph SVM for multiple fault state recognition. The experimental results showthat this method can realize the multiple faults diagnosis of turbine rotor vibration. M eanwhile, compared with the extracted eigenvectors based on EEM D energy method, the experiment proves that this method has more accurate recognition rate." @default.
- W2368794739 created "2016-06-24" @default.
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- W2368794739 date "2016-01-01" @default.
- W2368794739 modified "2023-09-23" @default.
- W2368794739 title "SVM Rotor Vibration Fault Diagnosis Based on EEMD Permutation Entropy" @default.
- W2368794739 hasPublicationYear "2016" @default.
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