Name
Title | ||
|---|---|---|
High-Voltage Circuit Breaker Fault Diagnosis Using A Hybrid Feature Transformation Approach Based On Random Forest And Stacked Autoencoder |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIE.2018.2879308 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS |
Keywords | DocType | Volume |
Fault diagnosis, feature transformation, high-voltage circuit breaker (HVCB), random forest (RF), stacked autoencoder (SAE), wavelet packet decomposition | Journal | 66 |
Issue | ISSN | Citations |
12 | 0278-0046 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suliang Ma | 1 | 2 | 1.08 |
| Mingxuan Chen | 2 | 2 | 0.75 |
| Wu Jianwen | 3 | 0 | 1.01 |
| Yuhao Wang | 4 | 170 | 38.41 |
| Bowen Jia | 5 | 2 | 1.08 |
| Yuan Jiang | 6 | 2 | 0.75 |