A pre-processing methodology to enhance novel information for rotating machine diagnostics

Abstract

Many sophisticated signal analysis techniques are developed to efficiently detect, localise and trend damage in rotating machine components such as bearings and gears for example. However, these techniques are generally applied without effectively incorporating historical information when performing condition monitoring. It is possible to enhance the performance of the analysis techniques by incorporating historical data from a machine in a reference condition. In this paper, a methodology is proposed to extract a novel signal i.e. a signal that contains information that is not present in the historical reference data, from a vibration signal. This is performed by utilising the available historical data. Sophisticated signal analysis techniques can subsequently be used on the novel vibration signal to diagnose the machine. The benefits of the methodology are illustrated on data, generated from phenomenological gearbox model data and experimental gearbox data, by utilising advanced techniques based on cyclostationary analysis. The results indicate that the novel vibration signal is more sensitive to damage, which highlights its potential as a pre-processing technique for rotating machine applications where historical data are available.