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Application of Iterative Singular Value Decomposition De-noising to Centrifugal Compressor Signal Analysis

Noise has great influence on the signal analysis for centrifugal compressor. In order to eliminate the noise, the iterative singular value decomposition (ISVD) de-noising is applied in this paper. Firstly, the algorithm of this method is introduced. It is based on singular value decomposition about the trajectory matrix of attractor which is reconstructed according to time delay embedding theory. Secondly, the accuracy of this method is tested by reconstructing the pseudo-phase portrait for the signal of Lorenz attractor. Comparing with the pseudo-phase portrait reconstructed from signal contained noise, the pseudo-phase portrait reconstructed after ISVD de-noising is more regular. Finally, this method is used in the centrifugal compressor signal analysis. By this method, the correlation dimension, which can reflect different fault condition for nonlinear system, is estimated accurately. It is proved that this method can reduce the noise effectively, which influences the correlation dimension computing a lot. This method can improve the nice rate of signal analysis.