融合CUSUM方法与BP神经网络的实际供热管网分级泄漏检测
Grade leakage detection in actual heating networks based on CUSUM method and BP neural network
摘要：
为解决目前供热管网泄漏故障检测困难、效率低的现状,本文提出了一种融合CUSUM(累积和)与BP神经网络(BPNN)的管网泄漏故障分级检测系统。该系统首先采用CUSUM方法(一级)检测供热管网补水流量并判断是否泄漏,如果该管网泄漏,则再采用BP神经网络(二级)对泄漏位置进行精确定位。以某矿区实际供热管网为研究对象,结合其供暖期内运行数据与仿真数据,以PCA(主成分分析)方法及数据归一化进行数据处理,构建并训练了实际供热管网泄漏位置检测的BPNN模型,最终开发了该矿区的CUSUM-BPNN供热管网泄漏故障分级检测系统。使用现场供回水管道排污阀对泄漏进行模拟,采用该系统对3个换热站及其供热管网分别进行了测试,结果表明,该系统能够准确判断泄漏故障并快速定位泄漏点所在管段,泄漏报警延迟时间在2 min之内,很少出现故障未报或者误报的情况,验证了本文所开发系统的可靠性和高效性。
Abstract：
To address the current difficulties and low efficiency in detecting leakage faults in heating networks, this paper proposes a grade leakage fault detection system for pipe networks that integrates CUSUM (cumulative sum) and BP neural network (BPNN). Firstly, the system employs the CUSUM method (first level) to detect the make-up water flow in the heating network and determine whether a leakage exists. If a leakage is detected, the BPNN (second level) is then utilized to precisely locate the leakage position. Taking the actual heating network in a mining area as the research object, and combining its operational data during the heating period with simulation data, data processing is carried out using PCA (principal component analysis) and data normalization methods to construct and train a BPNN model for detecting leakage positions in the actual heating network. Finally, a CUSUM-BPNN grade leakage fault detection system for the heating network in this mining area is developed. By using on-site blowdown valves of the supply and return water pipes to simulate leakages, the system is tested on three heat exchange stations and their corresponding heating networks. The results show that the system can accurately identify leakage faults and rapidly locate the pipe sections where the leakages occur, with leakage alarm delays within 2 minutes. There are few cases of undetected faults or false alarms, verifying the reliability and efficiency of the system developed in this paper.
Keywords:heating network; leakage detection; CUSUM; BP neural network(BPNN); simulation model; principal component analysis (PCA)