Volume 68

A Model-Data Fusion-Driven Method for Blockage Localization in Air-Conditioning Water Pipe Networks Lisheng Luo, Xuan Zhou, Jiaming Xiong, Junlong Xie

Abstract

Blockages in air-conditioning water pipe networks can increase hydraulic resistance, reduce energy distribution efficiency, and threaten the safe operation of air-conditioning systems. However, existing studies mainly focus on leakage localization in pipe networks, while research on blockage localization remains limited. To address the difficulty of obtaining sufficient fault data from real systems, this study proposes a model-data fusion-driven method for blockage localization in air-conditioning water pipe networks. In the proposed framework, measurement data from the supervisory control and data acquisition system are first used to calibrate the hydraulic model through impedance identification using a genetic algorithm. The calibrated model is then employed to simulate multiple blockage scenarios and generate a dataset for training a multilayer perceptron model. During diagnosis, the pressure variation rates of sensors before and after faults are used as input features to predict the most likely blockage components, and a candidate blockage component set based on the Top-k predicted probabilities is introduced to improve localization reliability. A case study shows that the deviations between simulated and measured values of the calibrated hydraulic model are within 1%. The trained multilayer perceptron model achieves a blockage localization accuracy of 90.52%. When the candidate set contains the two components with the highest predicted probabilities, the coverage of the actual blockage component increases by 6.02%. Expanding the candidate set to the three components with the highest predicted probabilities further improves the coverage by 1.76%. These results demonstrate that the proposed model-data fusion framework can effectively localize blockage components and support intelligent operation and maintenance of air-conditioning systems.

Keywords air-conditioning water pipe networks, blockage localization, model-data fusion, hydraulic modeling, machine learning