High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder

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 des...

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Vydáno v:IEEE transactions on industrial electronics (1982) Ročník 66; číslo 12; s. 9777 - 9788
Hlavní autoři: Ma, Suliang, Chen, Mingxuan, Wu, Jianwen, Wang, Yuhao, Jia, Bowen, Jiang, Yuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Binary codes
Circuit breakers
Circuit faults
Fault diagnosis
Feature extraction
feature transformation
High voltages
high-voltage circuit breaker (HVCB)
Machine learning
Mapping
Neural networks
Optimization
Radio frequency
random forest (RF)
stacked autoencoder (SAE)
Support vector machines
Transformations
Vibrations
Wavelet analysis
wavelet packet decomposition
Wavelet transforms
ISSN:0278-0046, 1557-9948
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Popis
Shrnutí: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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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2018.2879308