Acoustic Emission Signal Classification in Condition Monitoring Using the Kolmogorov-Smirnov Statistic.

Acoustic emission (AE) measurement at the bearings of rotating machinery has become a useful tool for diagnosing incipient fault conditions. In particular, AE can be used to detect unwanted intermittent or partial rubbing between a rotating central shaft and surrounding stationary components. This is a particular problem encountered in gas turbines used for power generation. For successful fault diagnosis, it is important to adopt AE signal analysis techniques capable of distinguishing between various types of rub mechanisms. It is also useful to develop techniques for inferring information such as the severity of rubbing or the type of seal material making contact on the shaft. It is proposed that modelling the cumulative distribution function of rub-induced AE signals with respect to appropriate theoretical distributions, and quantifying the goodness of fit with the Kolmogorov-Smirnov (KS) statistic, offer a suitable signal feature for diagnosis. This paper demonstrates the successful use of the KS feature for discriminating different classes of shaft- seal rubbing. A hierarchical cluster algorithm was employed for grouping extracted KS values. AE rub signals were simulated with various metallic seals and measured at the journal bearings of a test rig rotating at approximately 1500 rev/min. Also, the KS classification results were directly compared withmore established AE feature vectors.