期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 66, 期 12, 页码 9777-9788出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2018.2879308
关键词
Fault diagnosis; feature transformation; high-voltage circuit breaker (HVCB); random forest (RF); stacked autoencoder (SAE); wavelet packet decomposition
资金
- National Natural Science Foundation of China [51377007, 51677002]
- China Postdoctoral Science Foundation [2018M631307]
In recent years, machine learning techniques have been applied to test the fault type in high-voltage circuit breakers (HVCBs). Most related research involves in improving the classification method for higher precision. Nevertheless, as an important part of the diagnosis, the feature information description of the vibration signal of an HVCB has been neglected; in particular, its diversity and significance are rarely considered in many real-world fault-diagnosis applications. Therefore, in this paper, a hybrid feature transformation is proposed to optimize the diagnosis performance for HVCB faults. First, we introduce a nonlinear feature mapping in the wavelet package time-frequency energy rate feature space based on random forest binary coding to extend the feature width. Then, a stacked autoencoder neural network is used for compressing the feature depth. Finally, five typical classifiers are applied in the hybrid feature space based on the experimental dataset. The superiority of the proposed feature optimal approach is verified by comparing the results in the three abovementioned feature spaces.
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