Detection of District Heating Pipe Network Leakage Fault Using UCB Arm Selection Method

• Published in: Buildings (Basel) Vol. 11; no. 7; p. 275
• Main Authors: Shen, Yachen, Chen, Jianping, Fu, Qiming, Wu, Hongjie, Wang, Yunzhe, Lu, You
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Accuracy; Algorithms; Artificial intelligence; contextual bandit; Decision making; District heating; district heating pipe network; Electrical faults; Fault detection; Fault location; Heat; Heat pipes; Heating; Leakage; linear upper confidence bound; Machine learning; Methods; Neural networks; reinforcement learning; Sensors; Simulation; Troubleshooting; User needs
• DOI: 10.3390/buildings11070275
• ISSN: 2075-5309

District heating networks make up an important public energy service, in which leakage is the main problem affecting the safety of pipeline network operation. This paper proposes a Leakage Fault Detection (LFD) method based on the Linear Upper Confidence Bound (LinUCB) which is used for arm selection in the Contextual Bandit (CB) algorithm. With data collected from end-users’ pressure and flow information in the simulation model, the LinUCB method is adopted to locate the leakage faults. Firstly, we use a hydraulic simulation model to simulate all failure conditions that can occur in the network, and these change rate vectors of observed data form a dataset. Secondly, the LinUCB method is used to train an agent for the arm selection, and the outcome of arm selection is the leaking pipe label. Thirdly, the experiment results show that this method can detect the leaking pipe accurately and effectively. Furthermore, it allows operators to evaluate the system performance, supports troubleshooting of decision mechanisms, and provides guidance in the arrangement of maintenance.