# Part Qualification Methodology for Composite Aircraft Components Using Acoustic Emission Monitoring
## 2 Technical Approach
### Data Acquisition and Processes  
AE signals were monitored and recorded using a Physical Acoustics PCI8-Express data acquisition board. Prior to testing for record, pre-tests were conducted to calibrate the AE sensors, determine sensor placement, rehearse the test procedure, and collect noise data. Ambient noise levels and the effect of the loading fixture (e.g., friction) were tested and adequately controlled via test procedures (e.g., slow and steady load application, lubricated connections, etc.). In addition to the procedural controls, the data acquisition amplitude threshold was set above the noise floor and AE hit (i.e., wave packet) record timing parameters were set to minimize typical noise waveforms recorded during pre-testing. AE signals were uniformly pre-amplified across all sensor frequencies using 40 dB sensor-internal pre-amplification. Crane load-cell data was synchronously collected and correlated with AE signal data to aid in AE activity trend observation.  
As an example of the collected AE activity, Figure 2 showed representative waveforms collected from Test 8. Specifically, Figure 2a shows a low amplitude continuous AE signal, which is representative of noise recorded under zero load and demonstrably different compared to the burst-type signals typically associated with damage observed in Figure 2b,c. Figure 2b is an example of the type of AE signal recorded during the loading step, while Figure 2c shows an example of the type of waveforms recorded during unloading. In addition, Figure 2d shows the highest amplitude signal that was recorded at the time of final failure. Figure 2b,c both show similar wave characteristics including a burst of energy that occurs at 150 kHz while the signal obtained under no load showed that its energy  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test ID**} & \multicolumn{8}{c}{**Acoustic Emission Sensor ID and Location (in)**} \\ \cline{2-10}  & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** & **S10** \\ \hline
1 & - & - & - & - & - & - & - & - & - & - \\
2 & 3  
was distributed across many frequency values, similar to the final fracture signal, which however, had significantly higher amplitude and broader frequency content.  
As mentioned in the introduction, in general, there were two main AE data analysis approaches. The first leveraged the full recorded waveforms and the second relied on post-processing extracted features from such waveforms. For the analysis presented herein, raw AE signals similar to the ones shown in Figure 2 were parameterized via MISTRAS Group Inc. Noesis software (version 5.3, MISTRAS Group Inc., Princeton Junction, NJ, USA). The authors examined extracted AE features correlated with load data, including amplitude and absolute energy, to analyze AE activity trends and draw conclusions. AE signal energy was conceptually represented by the area under the rectified waveform, and was practically computed for discrete signal data by summing the square of the voltage amplitude over the signal length. To arrive at absolute energy with appropriate units (a), the squared voltage amplitude was further divided by the reference impedance over the signal duration. The cumulative absolute energy was the sum of the absolute energy for each AE hit, across all recorded hits during a test. The authors used this feature to characterize the test item performance, as it related to damage accumulation and ultimate failure. Additionally, the authors calculated the FR by identifying the load at which AE activity re-initiates during a loading cycle, divided by the previous maximum load reached. As discussed in the introduction, FR values less than one have been correlated with damage, and hence the authors used this parameter to further assess spar behavior.