BLIND SOURCE SEPARATION: A TOOL FOR ROTATING MACHINE MONITORING BY VIBRATIONS ANALYSIS?

Abstract

Blind source separation (BSS) is a general signal processing method, which consists of recovering, from a finite set of observations recorded by sensors, the contributions of different physical sources independently from the propagation medium and without any a priori knowledge on the sources. Such methods are attractive for the monitoring or the diagnosis of mechanical systems. It is shown that BSS allows the vibratory information generated from a single rotating machine working in a noisy environment to be recovered by freeing the sensor signal from the contribution of other working machines. In that way, BSS can be used as a pre-processing step to rotating machine fault detection and diagnosis. In this paper, two possible approaches to solve the BSS problem of rotating machine signals are compared; that is, the temporal or frequential approach. The first method developed initially for temporally white signals is used in an experimental context and it is shown that the results are comparable to the frequential domain approach specially developed for rotating machine signals. These two approaches are tested on real signals from a mechanical testing bench, and the implementations of the different methods as well as their performances are discussed. An example to bearing fault detection is given in the final part, to illustrate the potential of this approach as a pre-processing step to improve the diagnosis.