Against the backdrop of the accelerating global energy transition toward clean and low-carbon sources, hydrogen energy is emerging as a vital component of future energy systems due to its zero-carbon emissions, high energy density, and renewable nature. The safe and efficient transportation of hydrogen is a critical link in the hydrogen energy industry chain. As core connecting components in hydrogen transmission systems, the sealing integrity, hydrogen embrittlement resistance, and long-term service reliability of hydrogen pipeline joints directly impact the stable operation of entire hydrogen transmission systems and the feasibility of large-scale application. This study systematically reviews the research literature on hydrogen pipeline joints from 2014 to 2025 using bibliometric and knowledge graph analysis methods based on the Web of Science Core Collection database. It constructs co-occurrence networks and clustering graphs of keywords to identify core research themes in this field, including hydrogen embrittlement failure mechanisms, degradation of sealing material properties, structural design optimization of joints, and intelligent monitoring and fault diagnosis. Furthermore, this study highlights existing research gaps in evaluating joints’ long-term service performance, developing low-cost and efficient manufacturing technologies, and verifying reliability under complex operating conditions. This study provides a systematic bibliometric perspective on hydrogen pipeline joint technology development, aiding in identifying research frontiers and technological evolution pathways. It offers theoretical support and decision-making references for the safe construction and standardized development of hydrogen energy infrastructure.

The growing reliance on natural gas in urban China has heightened the urgency of maintaining pipeline integrity, particularly in environments prone to disruption by nearby construction activities. In this study, we present a practical approach for the real-time classification of distributed fiber optic monitoring signals, leveraging a hybrid framework that combines the feature learning capacity of a one-dimensional convolutional neural network (1D-CNN) with the classification robustness of a support vector machine (SVM). The proposed method effectively distinguishes various pipeline-related events—such as minor leakage, theft attempts, and human movement—by automatically extracting their vibration patterns. Notably, it addresses the common shortcomings of softmax-based classifiers in small-sample scenarios. When tested on a real-world dataset collected via the DAS3000 system from Hangzhou Optosensing Co., Ltd., the model achieved a high classification accuracy of 99.92% across six event types, with an average inference latency of just 0.819 milliseconds per signal. These results demonstrate its strong potential for rapid anomaly detection in pipeline systems. Beyond technical performance, the method offers three practical benefits: it integrates well with current monitoring infrastructures, significantly reduces manual inspection workloads, and provides early warnings for potential pipeline threats. Overall, this work lays the groundwork for a scalable, machine learning-enhanced solution aimed at ensuring the operational safety of critical energy assets.

Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was “safer”. The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks.

Oil depots in the coastal areas of China are prone to disasters caused by heavy rain due to the monsoon climate. Studies focusing on heavy rainfall vulnerability in coastal oil depots are limited. Therefore, we evaluated the safety of oil depots based on four factors in this study: personnel, equipment and facility, environment, and resilience. Complex networks, analytic hierarchy processes, and information entropy theory were used to establish an evaluation index system including four first-level indicators, nine second-level indicators, and 40 third-level indicators. Scores of 40 evaluation indicators were taken as the input, a vulnerability level of oil depots affected by heavy rain was gained as the output, and results were presented visually (different warning levels distinguished by color) to help oil depot enterprises improve their safety performance under extreme weather conditions.