Fault monitoring using acoustic emissions

Automatic monitoring techniques are a means to safely relax and simplify preventive maintenance and inspection procedures that are expensive and necessitate substantial down time. Acoustic emissions (AEs), that are ultrasonic waves emanating from the formation or propagation of a crack in a material, provide a possible avenue for nondestructive evaluation. Though the characteristics of AEs have been extensively studied, most of the work has been done under controlled laboratory conditions at very low noise levels. In practice, however, the AEs are buried under a wide variety of strong interference and noise. These arise due to a number of factors that, other than vibration, may include fretting, hydraulic noise and electromagnetic interference. Most of these noise events are transient and not unlike AE signals. In consequence, the detection and isolation of AE events from the measured data is not a trivial problem. In this paper we present some signal processing techniques that we have proposed and evaluated for the above problem. We treat the AE problem as the detection of an unknown transient in additive noise followed by a robust classification of the detected transients. We address the problem of transient detection using the residual error in fitting a special linear model to the data. Our group is currently working on the transient classification using neural networks.