Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2020, 3(7); doi: 10.25236/AJETS.2020.030719.

Research on conducted EMI noise diagnosis method based on Infomax-WT algorithm

Author(s)

Baitong Song, Wu Zhang, Zhou Chen, Hao Ma, Yakang Pei

Corresponding Author:
Baitong Song
Affiliation(s)

Nanjing Normal University, Nanjing, Jiangsu China

Abstract

In this paper, a diagnostic method of conducted EMI noise based on the Infomax-WT algorithm is proposed. Using collected conductive EMI noise samples, several independent noise signals are separated by Infomax. Each noise signal is subjected to wavelet transform to obtain the time-frequency diagram of each noise signal. The noise source is determined according to the frequency characteristic obtained by the time-frequency chart.

Keywords

electromagnetic compatibility, Infomax algorithm, wavelet transform, conducted interference, noise diagnosis

Cite This Paper

Baitong Song, Wu Zhang, Zhou Chen, Hao Ma, Yakang Pei. Research on conducted EMI noise diagnosis method based on Infomax-WT algorithm. Academic Journal of Engineering and Technology Science (2020) Vol. 3 Issue 7: 191-200. https://doi.org/10.25236/AJETS.2020.030719.

References

[1] Song Zhenfei. Detecting the number of EMI sources based on higher order statistics[C]. International Zurich Symposium on EMC. IEEE, 2009: 465-468.
[2] Wenfa Zhang. A denoising adaptive blind separation algorithm based on Infomax[D]. Huazhong University of Science and Technology, 2008.
[3] Xiaopei Wu, Zhongfu Ye, Qian Shen, Daoxin Zhang. On-line Infomax algorithm and its application in long recording EEG de-noising[J]. Journal of Circuits and Systems, 2005(05): 85-90.
[4] Deng Feiyue. Research on feature extraction and diagnosis method of rolling bearing fault [D]. North China Electric Power University (Beijing), 2016.
[5] Shang Hai kun, Yuan Jinsha, Wang Yu, Jin Song. Partial discharge feature extraction based on cross wavelet transform and correlation coefficient matrix[J]. Journal of Electrotechnical Technology, 2014, 29(04): 274-281.
[6] Duan Chendong, Gao Qiang, Xu Xianfeng. The application of frequency slice wavelet transform time-frequency analysis method in the fault diagnosis of generator set[J]. Proceedings of the CSEE, 2013, 33(32): 96-103+16.
[7] Yuan Lifen, Sun Yesheng, He Yigang. An analog circuit fault feature extraction method based on wavelet packet optimization[J]. Transactions of the China Electrotechnical Society, 2018, 33(01): 158-165.
[8] Xie Dong, Zhang Xing, Cao Renxian. Island detection technology based on wavelet transform and neural network[J]. Proceedings of the Chinese Society of Electrical Engineering, 2014, 34(04): 537-544.
[9] Ge Zhizhi, Chen Zhongsheng. Time-frequency analysis technology of Matlab and its application [M]. People's Posts and Telecommunications Press, 2006: 2-8.