Monitoring Failure of Composite Pressure Vessels with Acoustic Emissions

Abstract Two carbon fiber reinforced type IV pressure vessels are subjected to step-wise pressurization until burst, while monitored using acoustic emissions (AE). Unlike most prior studies, AE data is collected throughout the entire damage progression. The vessels, manufactured with differing parameters, failed in distinct composite layers – A-type in the hoop layers and B-type in the helical layers. The AE signals are evaluated to study material degradation and identify fiber breaks as signs of critical damage accumulation. The signals are distributed randomly across the surface, with localized accumulation only minutes before rupture, close to the rupture plane. The difference in manufacturing parameters did not result in any clear difference in the AE activity. Felicity and Shelby ratios show consistent decline with increasing pressure, suggesting potential for damage assessment and burst prediction. It is discussed how these ratios are affected by coupling quality of the AE sensors, the shape of the pressurization profile and prior loadings. Different signal features based on the amplitude and the frequency content are extracted for a classification into failure mechanisms. Based on previous studies, AE signals corresponding to fiber breaks have a characteristic high-frequency spectrum and show a delay in occurrence, with an increase in the number of breaking fibers towards the end of the experiment. Indeed, high frequency signals tend to occur later and signals in specific peak-frequency ranges (350 – 400 kHz, > 500 kHz) somewhat resemble the expected behavior. However, the dataset is too variable and too incongruent for any clear interpretations. Likely reasons are signal propagation effects, the complex composite structure, simultaneous occurrence of signals and measurement uncertainties. A review of relevant studies is provided to show that similar issues affect also previous works. Successfully identifying fiber breaks in large-scale, complex composite structures based on AE data, and turning this into an applicable health-monitoring technique, therefore remains a challenge.