The first four tests were recorded on the north east shaft and the remaining six runs were done two each on the remaining three shafts. The recorded data was filtered to remove low level background noise and source location was plotted for each of the runs. The first AE test run was recorded with only two channels to determine if guard sensors were needed. The clustering analysis used on these data was developed to allow acoustic emission to reliably detect flaw growth in welds during the welding process. It is based on observations of acoustic emission signal characteristics that accompany known crack growth under controlled laboratory conditions.
Cracks are also localized sources of acoustic emission.
In other words, the events produced by a crack have tightly clustered source locations. This algorithm has been successfully tested over the past 15 years in a wide variety of acoustic emission applications and has allowed AE to be utilized for applications that have large amounts of background noise. It is particularly useful in cases where noise sources are coincident with cracks such as cracks growing out of fastener holes. The data recorded during these tests had high background noise and the clustering algorithm allows us to see differences in the data for each of the shafts that probably would have been over looked in simpler analysis. To perform the analysis, we filtered the recorded data to remove low level noise and then computed source locations using the correct acoustic velocity as determined by our calibration process.