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Robust machine fault detection with independent component analysis and support vector data description

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3 Author(s)
Ypma, Alexander ; Dept. of Appl. Phys., Delft Univ. of Technol., Netherlands ; Tax, D.M.J. ; Duin, R.P.W.

We propose an approach to fault detection in rotating mechanical machines: fusion of multichannel measurements of machine vibration using independent component analysis (ICA), followed by a description of the admissible domain (part of the feature space indicative of normal machine operation) with a support vector domain description (SVDD) method. The SVDD method enables the determination of an arbitrary shaped region that comprises a target class of a dataset. In this particular application, it provides a way to quantify the compactness of the admissible class in relation to data preprocessing. Application to monitoring of a submersible pump indicates that combination of measurement channels with ICA gives improved results in fault detection, without requiring detailed prior knowledge on origin and type of the failure

Published in:

Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.

Date of Conference:

Aug 1999