Two-Level Diagnosis of Heating Pipe Network Leakage Based on Deep Belief Network

Abstract

This paper takes on the central heating secondary network and establishes a two-level diagnosis model of leakage fault of heating pipe network based on deep belief network (DBN) under the condition of constant and small supply flow quality regulation. Firstly, a leakage condition hydraulic calculation model of the heating pipe network is established with graph theory, which provides the pressure changes of the pressure monitoring points in the pipe network. Then, the first-level diagnostic model for the leakage of the heating pipe network is designed to diagnose the leaky pipe segment by using a deep belief network. Based on the results of the first-level diagnostic model, each leaky pipe segment is treated as a unit and a second-level diagnosis model is then developed to predict the specific leak location. Finally, the model is verified with a branch-pipe network and a loop-pipe network. Experimental results showed that the first-level diagnostic model had a high accuracy rate in the prediction of leaky pipe segments, which was superior to traditional fault diagnosis methods such as BP (Back Propagation Neural Network) and SVM (Support Vector Machines). The second-level diagnostic model can detect the leak location of the leaky pipe with satisfactory results.

Keywords

• Deep belief network
• heating pipe network
• fault diagnosis
• restricted Boltzmann machine