As detecting the pressure signal is the main method in the real-time leak diagnosis of long pipeline, an abnormal pressure diagnosis method is proposed to make the leak diagnosis rapidly and accurately. Firstly, a combination filter algorithm is designed to realize noise reduction. Then, an anomaly detection algorithm is designed to detect abnormal pressure on the head and tail of the pipeline. Finally, the relevancy of the detected novelties is computed by Pearson correlation coefficient to identify the leakages. The experimental results show that the proposed method can rapidly detect the leakage with few false alarms and accurately locate the position of the leakage.
leak diagnosis long pipeline adaptive threshold leak detection leak location
This is a preview of subscription content, log in to check access.
DANETI M. A practical preprocessing treatment for pipeline leak locating improving [C]//Proceedings of 2008 IEEE International Conference on Digital Object Identifier. [s.l.]: IEEE, 2008: 9–12.Google Scholar
FERRANTE M, BRUNONE B. Pipe system diagnosis and leak detection by unsteady-state tests. 2. Wavelet analysis [J]. Advances in Water Resources, 2003, 26(1): 107–116.CrossRefGoogle Scholar
GE C H, YANG H Y, YE H, et al. A fast leak locating method based on wavelet transform [J]. Tsinghua Science & Technology, 2009, 14(5): 551–555.MathSciNetCrossRefGoogle Scholar
LI X X, GE M R, DAI X L, et al. Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BEI Dou, and GALIEO [J]. Journal of Geodesy, 2015, 89(6): 607–635.CrossRefGoogle Scholar
VALIZADEH S, MOSHIRI B, SALAHSHOOR K. Multiphase pipeline leak detection based on fuzzy classification [J]. AIP Conference Proceedings, 2009, 1159(1): 72–80.CrossRefGoogle Scholar
MA Y J, LIU J H, WANG Z G. Modified fuzzy min-max neural network for clustering and its application on the pipeline internal inspection data [C]//Proceedings of 35th Chinese Control Conference (CCC). Chengdu: IEEE, 2016: 3509–3513.CrossRefGoogle Scholar
ZHANG H G, LIU J H, MA D Z, et al. Data-corebased fuzzy min-max neural network for pattern classification [J]. IEEE Transactions on Neural Networks, 2011, 22(12): 2339–2352.CrossRefGoogle Scholar
ZHANG H G, WANG J Y, WANG Z S, et al. Sampleddata synchronization analysis of Markovian neural networks with generally incomplete transition rates [J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(3): 740–752.CrossRefGoogle Scholar
LIU J H, ZHANG H G, FENG J, et al. A new fault detection and diagnosis method for oil pipeline based on rough set and neural network [J]. Lecture Notes in Computer Science, 2007, 4493: 561–569.CrossRefGoogle Scholar
XU D L, LIU J, YANG J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection [J]. Expert Systems with Applications, 2007, 32(1): 103–113.CrossRefGoogle Scholar
DA SILVA H V, MOROOKA C K, GUILHERME I R, et al. Leak detection in petroleum pipelines using a fuzzy system [J]. Journal of Petroleum Science and Engineering, 2005, 49(3/4): 223–238.CrossRefGoogle Scholar
ZHANG L B, QIN X Y, WANG Z H, et al. Designing a reliable leak detection system for west products pipeline [J]. Journal of Loss Prevention in the Process Industries, 2009, 22(6): 981–989.CrossRefGoogle Scholar
LIU J H, FENG J, TAN L, et al. An algorithm of autoupdate threshold for singularity analysis of pipeline pressure [J]. Mathematical Problems in Engineering, 2013, 2013: 495425.Google Scholar