Flow regime identification for air valves failure evaluation in water pipelines using pressure data.

Journal: Water research
Published Date:

Abstract

Air valve failure can cause air accumulation and result in a loss of carrying capacity, pipe vibration and even in some situations a catastrophic failure of water transmission pipelines. Air is most likely to accumulate in downward sloping pipes, leading to flow regime transition in these pipes. The flow regime identification can be used for fault diagnosis of air valves, but has received little attention in previous research. This paper develops a flow regime identification method that is based on support vector machines (SVMs) to evaluate the operational state of air valves in freshwater/potable pipelines using pressure signals. The laboratory experiments are set up to collect pressure data with respect to the four common flow regimes: bubbly flow, plug flow, blow-back flow and stratified flow. Two SVMs are constructed to identify bubbly and plug flows and validated based on the collected pressure data. The results demonstrate that pressure signals can be used for identifying flow regimes that represent the operational state (functioning or malfunctioning) of air valves. Among several signal features, Power Spectral Density and Short-Zero Crossing Rate are found to be the best indictors to classify flow regimes by SVMs. The sampling rate and time of pressure signals have significant influence on the performance of SVM classification. With optimal SVM features and pressure sampling parameters the identification accuracies exceeded 93% in the test cases. The findings of this study show that the SVM flow regime identification is a promising methodology for fault diagnosis of air valve failure in water pipelines.

Authors

  • Haixing Liu
    School of Hydraulic Engineering, Dalian University of Technology, Dalian, 116024, China.
  • Yan Zhu
    Department of Chemistry, Xixi Campus, Zhejiang University, Hangzhou, 310028, China. Electronic address: zhuyan@zju.edu.cn.
  • Shengwei Pei
    School of Hydraulic Engineering, Dalian University of Technology, Dalian, 116024, China.
  • Dragan Savić
    KWR Watercycle Research Institute, Nieuwegein, Netherlands; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
  • Guangtao Fu
    College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
  • Chi Zhang
    Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yixing Yuan
    School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, China.
  • Jinsong Zhang
    Department of Emergency, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China. zhangjso@njmu.edu.cn.