## Low Computation Acoustic Emissions Structural Health Monitoring Through Analog Signal Pre-Processing

Rune Schlanbusch, Eric Bechhoefer, and Thomas J. J. Meyer
Submission Type:
Full Paper
AttachmentSizeTimestamp
phmc_17_010.pdf1.63 MBAugust 18, 2017 - 10:28am

In this research an innovative acoustic emission sensing system has been developed intended for structural fatigue crack monitoring. The innovation lies in analog pre-processing of the detected acoustic emissions for signal enveloping, thus relying on cheap high bandwidth components. The technique is based on heterodyning the amplified acoustic emission signal with a carrier signal of a chosen frequency. Next, the signal is filtered and the signal envelope is obtained by phase shifting the signal by $\pi/2$ creating an analytic signal that is digitally sampled. This results in need of low sampling rate digital acquisition equipment giving relatively small amounts of data to be processed and stored, considerably reducing the system cost. This is particularly suitable for applications involving large or complex structures to be monitored, where a multitude of sensors are needed. The system was built and tested on aluminum test coupons during tension-tension fatigue. The envelope signal is filtered for background noise through thresholding based on statistical knowledge of the noise distribution. The accumulation of acoustic activity shows promising results with an early period of high acoustic activity during settling of the material that asymptotically converges at a certain level. At the next stage, there is no activity until a certain point is reached where a sudden ramp up of AE is detected close to the end of the experiment. Through extensometer measurement, the change in coupon length at this point in time strongly indicates that the ramp up of AE activity is due to crack initiation and propagation.

Publication Year:
2017
Publication Volume:
8
Publication Control Number:
010
Page Count:
7
Submission Keywords:
SHM
Acoustic Emissions
Submission Topic Areas:
Structural health monitoring
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