Blind source separation of noisy harmonic signals for rotating machine diagnosis

Blind source separation (BSS) consists of recovering signals from different physical sources from several observed combinations independently of the propagation medium. BSS is also a promising tool for non-destructive machine condition monitoring by vibration analysis, as it is intended to retrieve the signature of a single rotating machine from combinations of several working machines. In this way, BSS can be seen as a pre-processing step that improves the diagnosis. thods generally assume observations that are either noise-free or corrupted with spatially distinct white noises. In the latter case, principal component analysis (PCA) is applied as a first step to filter out the noise and whiten the observations. Obviously, the efficiency of the whole separation procedure depends on the accuracy of the first step (PCA). r, in the real world, signals of rotating machine vibration may be severely corrupted with spatially correlated noises and therefore the signal subspace will not be correctly estimated with PCA. The purpose of this paper is to propose a ‘robust-to-noise’ technique for the separation of rotating machine signals. The sources are assumed here to be periodic and so can be modelled as the sum of sinusoids of harmonic frequencies. A new estimator of the signal subspace and the whitening matrix is introduced which exploits the model of sinusoidal sources and uses spectral matrices of delayed observations to eliminate the influence of the noise. After whitening, the second step of source separation remains unchanged. Finally, performance of the algorithm is investigated with artificial data and experimental rotating machine vibration data.