为解决基于声信号分析的高压断路器在线诊断故障中外界环境干扰问题,提出了一种声音扰动信号辨识的盲源分离方法.首先利用改进的势函数法进行源数估计,然后通过集合经验模态分解(EEMD)算法得到多个IMF分量,重构形成符合聚类源数的多维信号,并利用拟牛顿法优化快速独立分量分析算法,实现声音信号的盲源分离;最后根据包络特征比对获取断路器状态辨识的合闸声音信号分量.实验结果证明,文章提出的方法能够在信源信息未知情况下,从混叠声音信号有效地提取断路器操动产生的有用声信号.%To solve the ambient interference of online fault diagnostics in high voltage circuit breaker which is based on acoustic signal analysis,a blind source separation method is proposed for sound disturbance signal recognition.An improved potential function is used to estimate sources,and then,the ensemble empirical mode decomposition (EEMD) is applied for calculating several intrinsic mode functions (IMF),and the multidimensional signals are reconstructed in line with the number of sources.The sound signal blind source separation is completed by fast independent component analysis (FastICA),which is optimized by a quasi-Newton method.The sound signal component of breaker closing state identification is obtained according envelope features comparison.The experimental results show that the proposed method can make the useful acoustic signal generated by the action of circuit breakers extracted effectively from mixed voice signal while the information of sources is unknown.
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