Cargando…
Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest
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 r...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948935/ https://www.ncbi.nlm.nih.gov/pubmed/29659548 http://dx.doi.org/10.3390/s18041221 |
_version_ | 1783322664707817472 |
---|---|
author | Ma, Suliang Chen, Mingxuan Wu, Jianwen Wang, Yuhao Jia, Bowen Jiang, Yuan |
author_facet | Ma, Suliang Chen, Mingxuan Wu, Jianwen Wang, Yuhao Jia, Bowen Jiang, Yuan |
author_sort | Ma, Suliang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5948935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59489352018-05-17 Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest Ma, Suliang Chen, Mingxuan Wu, Jianwen Wang, Yuhao Jia, Bowen Jiang, Yuan Sensors (Basel) Article 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. MDPI 2018-04-16 /pmc/articles/PMC5948935/ /pubmed/29659548 http://dx.doi.org/10.3390/s18041221 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ma, Suliang Chen, Mingxuan Wu, Jianwen Wang, Yuhao Jia, Bowen Jiang, Yuan Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest |
title | Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest |
title_full | Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest |
title_fullStr | Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest |
title_full_unstemmed | Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest |
title_short | Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest |
title_sort | intelligent fault diagnosis of hvcb with feature space optimization-based random forest |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948935/ https://www.ncbi.nlm.nih.gov/pubmed/29659548 http://dx.doi.org/10.3390/s18041221 |
work_keys_str_mv | AT masuliang intelligentfaultdiagnosisofhvcbwithfeaturespaceoptimizationbasedrandomforest AT chenmingxuan intelligentfaultdiagnosisofhvcbwithfeaturespaceoptimizationbasedrandomforest AT wujianwen intelligentfaultdiagnosisofhvcbwithfeaturespaceoptimizationbasedrandomforest AT wangyuhao intelligentfaultdiagnosisofhvcbwithfeaturespaceoptimizationbasedrandomforest AT jiabowen intelligentfaultdiagnosisofhvcbwithfeaturespaceoptimizationbasedrandomforest AT jiangyuan intelligentfaultdiagnosisofhvcbwithfeaturespaceoptimizationbasedrandomforest |