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Sensors 2018, 18(4), 1221; https://doi.org/10.3390/s18041221

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

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
These authors contributed equally to this work.
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Received: 19 March 2018 / Revised: 10 April 2018 / Accepted: 10 April 2018 / Published: 16 April 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
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Abstract

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 rate (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. View Full-Text
Keywords: high-voltage circuit breakers; mechanical fault diagnosis; wavelet packet decomposition; random forest algorithm; ensemble learning; feature space optimization high-voltage circuit breakers; mechanical fault diagnosis; wavelet packet decomposition; random forest algorithm; ensemble learning; feature space optimization
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Ma, S.; Chen, M.; Wu, J.; Wang, Y.; Jia, B.; Jiang, Y. Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest. Sensors 2018, 18, 1221.

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