Robust Machine Fault Detection with Independent Component Analysis and Support Vector Data Description

We propose a novel 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.