Qualitative and quantitative damage assessment of composite pressure vessels on the basis of acoustic emission parameters

Abstract Hydrogen storage composite pressure vessels may fail under sudden internal pressure increases or external structural impacts, potentially leading to explosions and other serious consequences. Acoustic emission (AE) detection technology offers notable advantages for identifying damage and evaluating the performance of composite pressure vessels. This paper conducts a hydraulic burst test on the Type IV glass fiber‐wound composite vessel to study the damage evolution behavior. A method that integrates qualitative analysis utilizing the Gaussian mixture model (GMM) AE monitoring with quantitative assessment grounded in AE rate process theory is proposed. The damage evolution period of the vessel was divided into three stages based on the AE characteristic parameters of rise time/maximum amplitude values, average frequency values, counts, and energy fluctuations. The GMM algorithm facilitated the classification of damage modes into two categories: brittle fracture and progressive degradation. The findings indicated that the GMM algorithm effectively differentiates between these two damage modes and captures the dynamic evolution of material damage. Rate process theory was employed to derive a fourth‐order polynomial functional relationship between the cumulative covariate values of the AE and the relative loads on the vessel. Finally, a linear relationship was established between the damage index D and the cumulative parameter values, demonstrating that the damage index D can effectively assess the incremental damage to the vessel. By combining qualitative and quantitative analyses, the method can accurately determine the damage degree of the vessel structure, thus providing an essential reference for damage monitoring and safety assessment of pressure vessels. Highlights Hydraulic burst test of composite vessel based on AE monitoring. Damage evolution is studied using qualitative and quantitative analyses. The GMM algorithm is used to cluster vessel damage modes. A damage analysis model is introduced to assess the incremental damage.