A subspace method for the blind extraction of a cyclostationary source: Application to rolling element bearing diagnostics

The need for blindly separating mixtures of signals arises in many signal processing applications. A class of solutions to this problem was recently proposed by the so-called blind source separation (BSS) techniques which rely on the sole knowledge of the number of independent sources present in the mixture. This paper deals with the case where the number of sources is unknown and statistical independence may not apply, but where there is only one signal of interest (SOI) to be separated, which is cyclostationary. It proposes a blind extraction method using a subspace decomposition of the observations via their cyclic statistics. This method is first developed for instantaneous mixtures and is then extended to the convolutive case in the frequency-domain where it does not suffer from the permutation problem as does classical BSS. Experiments on industrial data are finally performed and illustrate the high performance of the proposed method.