Localization of CO\(_2\) gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling

CO\(_2\) leakage from transmission pipelines in carbon capture and storage systems may seriously endanger the ecological environment and human health. Therefore, there is a pressing need of an accurate and reliable leak localization method for CO\(_2\) pipelines. In this study, a novel method based on the combination of a wavelet packet algorithm and a radial basis function network (RBFN) is proposed to realize the leak location. Multiple acoustic emission (AE) sensors are first deployed to collect leakage signals of CO\(_2\) pipelines. The characteristics of the leakage signals from the AE sensors under different pressures are then analyzed in both time and frequency domains. Further, leakage signals are decomposed into three layers using wavelet decomposition theory. Wavelet packet energy and maximum value, and time difference calculated by cross-correlation are selected as the input feature vectors of the RBFN. Experiments were carried out on a laboratory-scale test rig to verify the validity and correctness of the proposed method. Leakage signals at different positions under different pressures were obtained on the CO\(_2\) pipeline leakage test bench. Compared with the time difference of arrival method, the relative error obtained using the proposed method is less than 2%, which has certain engineering application prospects.