Signal processing techniques to improve an Acoustic Emissions sensor

Acoustic Emissions (AE) are stress waves produced by the sudden internal stress redistribution of material caused by changes in the internal structure of the material. Possible causes of these changes are crack initiation and growth, crack opening/closure, or pitting in monolithic materials (gear/ bearing material). Where as vibration can measure the effect of damage, AE is a direct measure of damage. Unfortunately, AE methodologies suffer from the need of high sample rates (4 to 10 Msps) and relatively immature algorithms for condition indictors (CI). This paper hypothesizes that the AE signature is the result of some forcing function (e.g. periodic motion in the case of rotating machinery). By using analog signal processing to demodulating the AE signature, one can reconstruct the information carried (e.g. modulation) by the AE signature and provide improved diagnostics. As most on-line condition monitoring systems are embedded system, analog signal processing techniques where used which reduce the effective sample rate needed to operate on the AE signal to those similarly found in traditional vibration processing systems. Further, by implementing another signal processing technique, time synchronous averaging, the AE signal is further enhanced. This allowed, for the first time, an AE signal to be used to identify a specific component within gearbox. This processing is tested on a split torque gearbox and results are presented.