Leakage Diagnosis Technologies for Heating Pipe Networks: A Comprehensive Review of Currently Used Methods

Abstract

The development of district heating systems (DHSs) has increased the demand for leakage diagnosis in heating networks due to its impact on thermal efficiency, heating effectiveness, and security. This paper introduces various leakage diagnosis methods, provides solutions to the challenges of current diagnosis methods, and makes suggestions for future research. Internal methods include transient analysis, machine learning (ML), and negative pressure wave (NPW) technology. External methods include unmanned aerial vehicle (UAV) infrared thermography (UAIT), acoustic sensing, and fiber optic sensing methods. Some of these methods diagnose leakages through mathematical modeling and simulations, while others use various sensors to monitor changes in the internal medium of the pipeline to identify leakages. Additionally, UAIT and other special equipment are employed for leakage diagnosis. Detailed diagnosis principles of these methods as well as the solutions provided to address existing diagnosis bottlenecks were also introduced. Furthermore, this paper also reviews the performance of these diagnosis methods in terms of sensitivity, resolution, monitoring, accuracy, and cost. Based on the characteristics of each method, it offers guidance on the selection of pipeline leakage diagnosis methods for practical engineering applications.