快速独立分量分析算法在局放超声阵列信号去噪中的应用

根据阵列信号去噪需要最大限度保留"相位差"信息的要求,提出采用快速独立分量分析(fast independent component analysis,FastICA)对含噪局放超声阵列信号进行去噪处理。首先通过分离矩阵U实现混合信号中源信号与噪声信号的逐次分离;然后采用波形相似性原则对分离后的信号进行盲抽取,获得去噪后局放超声信号;在此基础之上,对局放超声阵列信号模型进行了重构。同时,提出以阵列流型平均相角差、波形变化趋势、波形相似性以及去噪前后的信噪比等参数对局放超声阵列信号的去噪效果进行综合评价。最后,将FastICA用于实验室实测局放超声阵列信号的去噪。结果表明,FastICA能够很好的抑制局放超声阵列信号中的白噪声,在保证高信噪比的情况下,可最大限度地保留阵列信号的"相位差"信息,确保良好的测向效果。

Application of the FastICA Algorithm to PD Ultrasonic Array Signal De-noising

According to the requirement of array signals de-noising,it needs to reserve the information of "phase difference" in the maximum limit.The author utilized complex FastICA algorithm to de-noise the partial discharge(PD) ultrasonic array signals containing noise in this paper.Firstly,the author realized the successive separation of source signal and noise signal in mixed signals through the complex separable matrix U;then adopted the waveform similarity principle to extract separated signals blindly,and got the PD ultrasonic signals after the de-noising;on this basis,according to the sensor array model,the author made physical reconstruction and got array signal model.At the same time,taking array flow pattern’s average phase angle difference(PAD) as a core,combining the variational trend parameter(VTP),normalized correlation coefficient(NCC) and the signal to noise ratio(SNR) before and after the de-noising to evaluate the simulation PD ultrasonic array signals synthetically.Finally,the author utilized the FastICA algorithm to de-noise the actual measured PD ultrasonic array signals in the lab.The results show that FastICA algorithm can restrain the white noise in PD ultrasonic array signals,with the premise of high SNR,it can reserve the information belonging to phase difference of array signals in the maximum limit and ensure the good result of direction and location.