Acoustic emission-based leak detection in gas storage and transportation facilities: Bibliometric analysis and review

The secure operation of gas storage and transportation facilities, as essential infrastructure of modern energy systems, is closely related to energy supply and public safety. Due to complex operating conditions, leakage from these facilities may cause serious safety, economic, and environmental consequences. Acoustic emission technology, owing to its non-invasive characteristics, high sensitivity, and real-time monitoring capability, has shown potential for gas leak monitoring in storage and transportation facilities. Nevertheless, studies still lack a systematic bibliometric and technical synthesis of the evolution, methodological structure, engineering challenges, and future trends of acoustic emission-based gas leak monitoring. In this review, 161 records are initially retrieved from the Web of Science Core Collection. After screening, this review retains 155 publications within the analysis period from January 1, 2005 to May 1, 2026. VOSviewer, Pajek, Gephi, and CiteSpace are used to analyze publication trends, collaboration networks, sources, keyword co-occurrence, cluster validation, keyword timeline evolution, and keyword bursts. Furthermore, the results reveal three technological stages: foundational exploration of acoustic emission-based leak detection, methodological development of signal processing and pattern recognition, and intelligent advancement of acoustic emission-based leak monitoring methods. Moreover, the technical synthesis shows that current research is organized around signal processing and numerical simulation, machine learning-based leak identification, and acoustic emission-based leak localization. Finally, based on the identified engineering constraints and methodological gaps, this review summarizes future directions involving advanced signal processing and multi-medium data-source expansion under complex coupled environments, multi-strategy fusion and hardware-software co-optimization, and AI-based localization technology for full-cycle and multi-scenario leak monitoring.