Blind source separation for acoustical machine diagnosis

Acoustical machine diagnosis is frequently made difficult by noisy environments at a production site. This paper evaluates whether blind source separation (BSS) algorithms can be used to enhance machine signals as they enhance speech signals. Unfortunately, the SNR is not significant, since as an energy based number it ignores distortions of the machine signal. In comparison to speech processing where a small distortion does not reduce the intelligibility, it reduces the classification rate in machine diagnosis significantly. Therefore, an assessment for BSS algorithms with respect to machine diagnosis is proposed and used to verify the applicability of a new BSS algorithm.