Application of over-complete ICA in separating turbine vibration sources
An Hongwen, Yibing Liu, Yan Keguo, Yu Wang, Huan Ping Yang · 2012
Over-complete ICA problem are always met in engineering applications. That is to say, the number of unknown sources is more than the number of observed signals. At this time basic ICA model is not suitable. This text utilizes the component of priori knowledge as additional input signal (addition virtual channel), to increase the number of the input signals. And it can solve the engineering application problem of over-complete ICA. This method is tested through a group of actual turbine vibration signals. The similarity coefficient is introduced to verify the effect of source separation.