LAUSR

Abstract Vibro-acoustic signatures are widely used for diagnostics of rotating machinery. Vibration based automatic diagnostics systems need to achieve a good separation between signals generated by different sources. The separation task may be challenging, since the effects of the different vibration sources often overlap. In particular, there is a need to separate between signals related to the natural frequencies of the structure and signals resulting from the rotating components (signal whitening), as well as a need to separate between signals generated by asynchronous components like bearings and signals generated by cyclo-stationary components like gears. Several methods were proposed to achieve the above separation tasks. The present study compares between some of these methods. The paper also presents a new method for whitening, Adaptive Clutter Separation, as well as a new efficient algorithm for dephase, which separates between asynchronous and cyclo-stationary signals. For whitening the study compares between liftering of the high quefrencies and adaptive clutter separation. For separating between the asynchronous and the cyclo-stationary signals the study compares between liftering in the quefrency domain and dephase. The methods are compared using both simulated signals and real data.