Statistical distribution models for monitoring acoustic emission (AE) energy of abrasive particle impacts on carbon steel

The estimation of energy dissipated during multiple particle impact is a key aspect in evaluating the abrasive potential of particle-laden streams. This paper reports the results of systematic acoustic emission measurements in which a particle laden airflow was directed at a target plate. The impingement conditions were chosen to limit the amount of overlap of particle arrival events in order to develop a model of the stream as the cumulation of individual particle arrival events. To this end, some limited experiments were done with individual particles.The probability distribution of particle impact energy was obtained for a range of particle sizes and impact velocities. Two methods of time series processing were investigated to isolate the individual particle arrivals from the background noise and from particle noise associated with contact of the particles with the target after their first arrival. For the conditions where it was possible to resolve individual impacts, the probability distribution of particle arrival AE energy was determined by the best-fit lognormal probability distribution function. The mean and variance of this function was then correlated with the known nominal mass and impact speed to give a semi-quantitative assessment of particle impact energy.A pulse shape function was devised for the target plate by inspection of the records, backed up by pencil lead tests and this, coupled with the energy distribution functions allowed the records to be simulated knowing the arrival rate and the nominal mass and velocity of the particles. A comparison of the AE energy between the recorded and simulated records showed that the principle of accumulating individual particle impact signatures could be applied to records even when the individual impacts could not be resolved.

► Application of acoustic emission (AE) to the monitoring of particle impacts.
► Use of probability distribution functions to describe the AE energy per particle.
► Signal processing techniques to obtain particle AE signatures for relatively sparse streams.
► Calibration of AE energy with distributions of particle mass and velocity.
► Simulation of time series for particle streams, including those for which individual particles cannot be resolved.