Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

Suliang Ma, Mingxuan Chen, Jianwen Wu, Yuhao Wang, Bowen Jia, Yuan Jiang
2018 Sensors  
Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy
more » ... e (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.
doi:10.3390/s18041221 pmid:29659548 pmcid:PMC5948935 fatcat:lx3bjv27krbojde42u4wkklq6e