Mechanical fault diagnosis of high voltage circuit breaker based on CEEMDAN sample entropy and FWA-SVM

被引：0

作者：

Zhao S. ^{[1
]}

Ma L. ^{[1
]}

Zhu J. ^{[2
]}

Li J. ^{[3
]}

Zhao H. ^{[1
]}

机构：

[1] School of Electrical & Electronic Engineering, North China Electric Power University, Baoding

[2] Guiyang Huaxi Power Supply Bureau of Guizhou Grid Company, Guiyang

[3] Maintenance Branch of State Grid Hebei Electric Power Company, Shijiazhuang

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2020年 / 40卷 / 03期

关键词：

Complete ensemble empirical mode decomposition with adaptive noise; Fault diagnosis; High voltage circuit breaker; Support vector machines; Vibration signal;

D O I：

10.16081/j.epae.202002004

中图分类号：

学科分类号：

摘要：

Aiming at the problem that feature extraction is easy to be affected by jamming signal in the process of mechanical fault identification based on vibration signal of circuit breaker, a fault feature extraction method that combines CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) and sample entropy is proposed. Several IMF(Intrinsic Mode Function) components that reflect the mechanical state information of operating process are extracted by CEEMDAN. The top 7th order components are selected based on the energy distribution and correlation coefficients and denoised by wavelet packet soft threshold, and their sample entropies are calculated as the feature quantities. The FWA(FireWorks Algorithm) based on immune concentration is used to optimize the support vector machine classifier to identify the different operating states of the circuit breaker. The experimental results show that the features based on CEEMDAN sample entropy extraction are not sensitive to signal interference, and the FWA-SVM diagnosis method has a good effect on fault identification of high voltage circuit breakers. © 2020, Electric Power Automation Equipment Editorial Department. All right reserved.

引用

页码：181 / 186

页数：5

共 20 条

[1]

Yang Q., Ruan J., Zhuang Z., Et al., A new vibration analysis approach for detecting mechanical anomalies on power circuit breakers, IEEE Access, 7, pp. 14070-14080, (2019)

[2]

Li S., Wang F., Geng J., Et al., Mechanical state detection of high voltage circuit breaker based on optimized VMD, Electric Power Automation Equipment, 38, 11, pp. 148-154, (2018)

[3]

Chen W., Fan H., Wang Y., Et al., Identification method of circuit breaker vibration signal based on wavelet energy and neural network, Electric Power Automation Equipment, 28, 2, pp. 29-32, (2008)

[4]

Zhang J., Han P., Dong Z., Et al., Research on energy leakage in vibration signal analysis based on wavelet transform, Proceedings of the CSEE, 24, 10, pp. 238-243, (2004)

[5]

Zhao H., Li L., Early fault diagnosis method for wind turbine bearing based on MCKD-EMD, Electric Power Automation Equipment, 37, 2, pp. 29-36, (2017)

[6]

Zhu S., Shang W., Envelope algorithm in empirical mode decomposition, Fire Control and Command Control, 37, 9, pp. 125-128, (2012)

[7]

Hu A., Sun J., Xiang L., Modal aliasing problem in empirical mode decomposition, Journal of Vibration, Measurement & Diagnosis, 31, 4, pp. 429-434, (2011)

[8]

Qi T., Qiu Y., Wu Y., Application of EEMD to suppression of mode mixing in oscillation signals, Noise and Vibration Control, 30, 2, pp. 103-106, (2010)

[9]

Cai Y., Li A., Xu B., Et al., Adaptive guideline of ensemble empirical mode decomposition with gauss white noise, Journal of Vibration, Measurement & Diagnosis, 31, 6, pp. 709-714, (2011)

[10]

Torres M.E., Colominas M.A., Schlotthaue G., Et al., A complete ensemble empirical mode decomposition with adaptive noise, IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), pp. 4144-4147, (2011)

← 1 2 →