Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic

Abstract

Under the only hypothesis of independent sources, blind source separation (BSS) consists of recovering these sources from several observed mixtures of them. As it extracts the contributions of the sources independently of the propagation medium, this approach is usually used when it is too difficult to modelise the transfer from the sources to the sensors. In that way, BSS is a promising tool for non-destructive machine condition monitoring by vibration analysis. Principal component analysis (PCA) is applied as a first step in the separation procedure to filter out the noise and whiten the observations. The crucial point in PCA and BSS methods remains that the observations are generally assumed to be noise-free or corrupted with spatially white noises. However, vibration signals issued from electro-mechanical systems as rotating machine vibration may be severely corrupted with spatially correlated noises and therefore the signal subspace will not be correctly estimated with PCA. This paper extends a robust-to-noise technique earlier developed for the separation of rotating machine signals. It exploited spectral matrices of delayed observations to eliminate the noise influence. In this paper, we focus on the modulated sources and prove that the proposed PCA is available to denoise such sources as well as sinusoidal ones. Finally, performance of the algorithm is investigated with experimental vibration data issued from a complex electro-mechanical system.