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| Fault diagnosis of porcelain post insulator based on Gaussian mixture model of vibration signal spectrum |
| JIAO Zonghan1, SHAO Xinming2,3, ZHENG Xin1, LIU Ronghai1 |
1. Electric Power Research Institute of Yunnan Power Grid Co., Ltd, Kunming 650217;
2. Graduate Workstation of Yunnan Power Grid Company, North China Electric Power University, Kunming 650217;
3. Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention, North China Electric Power University, Baoding, Hebei 071003 |
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Abstract A vibration signal feature extraction method based on Gaussian mixture model (GMM) of vibration signal spectrum is proposed, and the extreme learning machine (ELM) optimized by particle swarm optimization (PSO) is used to realize fault state recognition and classification. Firstly, the frequency spectrum of the vibration signal of porcelain post insulator is obtained. Then, Gaussian probability density function and expectation maximization algorithm (EM) are used to divide the frequency spectrum into three modes. Three characteristic parameters, standard deviation σ, weight coefficient α and mean μ, can be obtained for each mode to characterize the modal bandwidth, modal component proportion and modal center frequency. Finally, the characteristic parameters of each mode are input into the classification model as eigen values to realize state recognition and classification.
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Received: 28 September 2020
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| Cite this article: |
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JIAO Zonghan,SHAO Xinming,ZHENG Xin等. Fault diagnosis of porcelain post insulator based on Gaussian mixture model of vibration signal spectrum[J]. Electrical Engineering, 2021, 22(6): 36-42.
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| URL: |
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https://dqjs.cesmedia.cn/EN/Y2021/V22/I6/36
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2021, Vol. 22 
