Blind Separation of rotating machine signals using Penalized Mutual Information criterion and Minimal Distortion Principle

Mohammed El Rhabi (1) , Hassan Fenniri (2) , Guillaume Gelle (2) , Georges Delaunay (2)

1 IMI - Département Ingénierie Mathématique et Informatique, École des Ponts ParisTech
2 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804

The Blind Separation problem of convolutive mixtures is addressed in this paper. We have developed a new algorithm based on a penalized mutual information criterion recently introduced in [El Rhabi et al., A penalized mutual information criterion for blind separation of convolutive mixtures, Signal Processing 84 (2004) 1979–1984] and which also allows to choose an optimal separator among an infinite number of valid separators that can extract the source signals in a certain sense according to the Minimal Distortion Principle. So, the minimisation of this criterion is easily done using a direct gradient approach without constraint on the displacements. Thus, our approach allows to restore directly the contribution of the sources to the sensor signals without post-processing as it is usually done. Finally, we illustrate the performances of our algorithm through simulations and on real rotating machine vibration signals.

Keywords: Fault detection; Condition monitoring; Signal Processing; Mechanical systems; Rotating machine; Blind source separation; Minimal Distortion Principle; Mutual Information

Domains: Mathematics [math]; Statistics [math.ST]; Information Theory [cs.IT]; Signal and Image Processing

Mohammed El Rhabi, Hassan Fenniri, Guillaume Gelle, Georges Delaunay. Blind Separation of rotating machine signals using Penalized Mutual Information criterion and Minimal Distortion Principle. Mechanical Systems and Signal Processing, 2005, 19 (6), pp.1282 - 1292. ⟨10.1016/j.ymssp.2005.08.028⟩. ⟨hal-01812922⟩