Acoustic emission-based damage characterization of 70 MPa type IV hydrogen composite pressure vessels during hydraulic tests. (23rd August 2019) - Liao, B.B.; Wang, D.L.; Hamdi, M.; Zheng, J.Y.; Jiang, P.; Gu, C.H.; Hong, W.R.
Abstract: This paper aims to characterize the damage mechanisms of 70 MPa Type IV hydrogen composite pressure vessels using the acoustic emission (AE) method. First, AE signals were captured during the 0–105 MPa and 0–158 MPa hydraulic tests of two vessels using multi-step loading method. Second, the AE feature parameters in time-domain and frequency-domain such as amplitude, frequency, and energy are studied. A multi-parameter statistical analysis (MPSA) method based on empirical mode decomposition (EMD) and K -means algorithm is performed to cluster AE events for the vessels. Intrinsic mode functions (IMFs) are decomposed by EMD and three IMFs with high frequency are chosen to reconstruct the feature parameters and provide signal pre-processing for K -means clustering analysis. Based on the relationship between AE features and damage modes, three main clusters with separate amplitude, absolute energy, and energy are correlated to matrix cracking, fiber/matrix debonding, and fiber breakage damage mechanisms. Besides, the effectiveness of MPSA method for signal classification is validated by principal component analysis (PCA) and fast Fourier transformation (FFT) method. Finally, the AE feature parameters such as amplitude and counts to peak for the three main damage modes are studied for the hydraulic proof tests and the burst tests to explore the damage evolution behaviors of the vessels with pressure increasing. Results show that AE method can be reliably used to characterize damage evolution mechanisms in composite pressure vessels. Highlights: AE signals were captured during the hydraulic tests using multi-step loading method. A multi-parameter statistical analysis method is performed to cluster AE events. Three resulting clusters were correlated to composite damage mechanisms. The effectiveness of MPSA method for signal classification is validated. AE feature parameters are studied to explore the damage evolution behaviors.