Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels Using Mathematically Modeled Acoustic Emission Failure Mechanism Data

Abstract The purpose of this research was to predict burst pressures in composite overwrapped pressure vessels (COPVs) by using mathematically modeled acoustic emission (AE) data. Both backpropagation neural network (BPNN) and multiple linear regression (MLR) analyses were performed on various subsets of the low proof pressure AE data to predict burst pressures and to determine if the two methods were comparable. AE data were collected during hydrostatic burst testing on the 15-inch diameter COPVs. Once collected, the AE data were filtered to eliminate noise then classified into AE failure mechanism data using a MATLAB Kohonen self-organizing map (SOM). The matrix cracking only amplitude distribution data were mathematically modeled using bounded Johnson distributions with the four Johnson distribution parameters – ϵ, λ, γ, and η – employed as inputs to make both the BPNN and MLR predictions. The burst pressure predictions generated using a MATLAB BPNN resulted in a worst case error of 1.997% as compared to −1.666% for the MLR analysis, suggesting comparability. However, the MLR analysis required the data from all nine COPVs to get approximately the same results as the BPNN training on just five COPVs; plus, MLR analyses are intolerant to noise, whereas BPNNs are not. Keywords: acoustic emissionburst pressure predictioncomposite overwrapped pressure vesselsmultiple linear regressionneural networks This article is part of the following collections: Research in Nondestructive Evaluation Outstanding Paper Award ACKNOWLEDGMENTS Thanks must be extended to James L. Walker II, Chief, Non-Destructive Evaluation Branch, NASA Marshall Space Flight Center, EM20, MFSC, Alabama 35812 ((256) 961–1784; James L. Walker@nasa.gov) for providing the original AE data and experimental setup information for this research.