Abstract
To maximize use of the valid features of time series signal images, and to detect oil pipeline leaks rapidly, accurately and reliability, the theory of data field is used for its advantages in clustering and singular value recognition. First, the feasibility of using acoustic wave signals for leak detection is demonstrated. Then, the semi-hard semi-soft thresholding function is used for de-noising. This method not only reduces the constant deviation in wavelet-based soft thresholding and hard thresholding, but also preserves the original features of signals and makes the de-noised signals smooth. Finally, the application of data field theory for leak detection and localization is analyzed and an improved algorithm based on data field theory is proposed. And the accuracy and universality of the proposed algorithm are verified through experiments. It is found that the adjusting parameters, influence factors and the width of the sliding window only affect the amplitude of the potential curve. That is, the localization of leak signals is not affected. Research shows that the proposed algorithm is a simple and effective new method for pipeline leak detection and localization besides correlation algorithm.
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References
Ellul, I.R.: Advance in pipeline leak detection. Pipeline Eng. ASME 34, 15–19 (2005)
Van Hieu, Bui, Choi, Seunghwan, Kim, Young Uk, et al.: Wireless transmission of acoustic emission signals for real-time monitoring of leakage in underground pipes. KSCE J. Civil Eng. 15(5), 800–822 (2011)
Mao, H.J., Li, W., Feng, X.L.: Investigation of method for pipeline leak detection and location based on EMD and cross-correlation. Sci. Technol. Eng. 10(19), 4532–4628 (2010)
Ge, C.H., Wang, G.Z., Ye, H., et al.: Leak location based on generalized correlation analysis. Inf. Control 38(2), 194–205 (2009)
Lian, L.J., Lin, W.G., Wu, H.Y.: Liquid-chlorine leak detection method based on power spectrum comparison. CIESC J. 64(12), 4461–4467 (2013)
Ma, B.X., Pan, H.X., Yang, S.M.: Gearbox fault diagnosis based on EEMD and two-dimensional marginal spectrum entropy. Veh. Power Technol. 4, 39–43 (2013)
Ekuakille, A.L., Vergallo, P.: Decimated signal diagonalization method for improved spectral leak detection in pipelines. IEEE Sensors 14(6), 1741–1748 (2014)
Ren, X.P., Yao, A.Q., Ren, X.K.: Analysising the method of finding leaking point in pipelines based on wavelat transformation in Matlab. J. Hebei Normal Univ. 31(2), 200 (2007)
Sun, L.Y., Li, Y.B., Qu, Z.G., et al.: Study on acoustic emission pipeline leaking detection based on EMD signal analysis method. Piezoelect. AcousZGtooptics 30(2), 239–241 (2008)
Li, W., Kuang, P., Li, Y.: A pipeline leak detection method based on fuzzy neural network. Comput Simul. 29(2), 232–290 (2009)
Feng, J., Zhang, H.G.: Diagnosis and localization of pipeline leak based on fuzzy decision-making method. Acta Autom. Sin. 31(3), 484–490 (2005)
Silva, D.H.V., Morooka, C.K., Guilherme, I.R.: Leak detection in petroleum pipelines using a fuzzy System. J. Pet. Sci. Eng. 49(4), 223–238 (2005)
Wang, M.D., Zhang, L.B., Liang, W., Chen, Z.G.: Pipeline leakage detection method based on independent component analysis and support vector machine. Acta Pet. Sin. 31(4), 559–663 (2010)
Ma, J.W., Liu, S.H., Ma, C.F.: The analysis of vector angles in remotely sensed data field and it’s application. J. Remote Sensing 5(1), 17–21 (2001)
Wang S L.: Data field and cloud model based spatial data mining and knowledge discovery. Ph.D. Thesis, Wuhan University, China, (2002)
Li, D.R., Wang, S.L., Li, D.Y.: The spatial data mining theoty and it’s application. Science Press, Beijing (2006)
Wang, S.L., Wu, J.B., Cheng, F., et al.: Behavior mining of spatial objects with data field. Geo-spatial Inf. Sci. 12(3), 202–211 (2009)
Sun, G.Y., Zhang, A.Z., Wang, Z.J.: Edge detection for multispectral image based on data firld model. J. Southeast Univ. 43(Sup(I)), 77–80 (2013)
Li, K., Tian, S.L., Geng, L.J., et al.: Facial feature extraction basedon data field. J. Northwest Univ. Natl. 30(12), 32–36 (2009)
Wu, J.B.: Study on image feature extration based on cloud model and data field. Ph.D. Thesis, Wuhan University, China, (2010)
Wu, T., Chen, Y.X., Yang, J.J.: Data field-based feature extraction method for sparse image. Comput. Sci. 41(10), 310–316 (2014)
Wang, S.L., Zou, S.S., Cao, B.H., et al.: Facial expression recognition based on data field. Geomat. Inf. Sci. Wuhan Univ. 35(6), 738–742 (2010)
Wang, Y.X., Zhao, J.M., Zheng, Z.L., et al.: A palmprint recognition method based on data fields and wavelet packet entropy. J. Nanjing Univ. 51(1), 174–180 (2015)
Li, N., Li, Y.X.: Image segmentation with two-dimension threshold based on adaptive particla swarm optimization and data field. J. Comput. Aided Des. Comput. Gr. 24(5), 628–635 (2012)
Su, R., Wang, Y.: Application of data field in network topology modeling. J. Guilin Electr. Technol. 28(6), 516–518 (2008)
Gao, Z.K., Jin, N.D.: Detecting community structure in complex networks based on K-means clustering and data field theory. Control Decition 24(3), 377–382 (2009)
Gan, W.Y., He, N., Li, D.Y., et al.: Community discovery method in networks based on topological potential. J. Softw. 20(8), 2241–2254 (2009)
Wang, L.J., Yang, B.R., Xie, Y.H.: Algotithm of community detection based on data fields. Appl. Res. Comput. 28(11), 4142–4145 (2011)
Li, X.S.: Study on classification and clustering mining based on cloud model and data field. Ph.D. Thesis, PLA University of Science and Technogy, China, (2003)
Fu, H.D., Li, X.: Dynamic recognition algotithm based on data field in immune intrusion detection. Comput. Appl. 27(9), 2160–2162 (2007)
Fu, J.M., Yu, Q.L., Yang, C.: Network security risk fusion model based on data field. Comput. Sci. 369(5), 72–75 (2009)
Liu, Y.L., Tang, X., He, J.H.: Spatial analysis based on data field and it’s application to land grade. Geomat. Inf. Sci. Wuhan Univ. 34(9), 1009–1013 (2009)
Tian, Y.G., Du, Y.H., Qin, D.H., et al.: Flood risk evaluation method based on data field and cloud modal. China Saf. Sci. J. 21(8), 158–163 (2011)
Hou, C.X., Zhang, E.H.: Pipeline leak detection based on double sensor negative pressure wave. Appl. Mech. Mater. 313–314, 1225–1228 (2013)
How, Q.M., Ren, L., Jiao, W.L., et al.: An improved negative pressure wave method for natural gas pipeline leak location using FBG based strain sensor and wavelet transform. Hindawi Publishing Corporation. Math. Probl. Eng. 2013(278794), 8 (2013). https://doi.org/10.1155/2013/278794
Osrapkowicz, P.: Leakage detection from liquid transmission pipelines using improved pressure wave techinque. Eksploatacjai Niezawodnosc-maintenance Reliab. 16(1), 9–16 (2014)
Fanisulaima, M., Abdullah, F., Jali, M.H., et al.: A feasibility study of internal and external based system for pipeline leak detection in upstream petroleum industry. Aust. J. Basic Appl. Sci. 8(3), 204–210 (2014)
Yang, R.G.: Research on leak detection and localization technology for long distance crede oil pipeline. Ph.D. Thesis, Nanjing University of Science & Technology, China, (2011)
Acknowledgements
Foundation item: the project supported by the Scientific Searching Plan Project of Shaanxi Province Education Department (No. 16JK1184) and the Project Foundation of Shaanxi Xueqian Normal University (No. 2016YBKJ074). Authors are grateful to the related departments for the financial supports to carry out this work.
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Liu, W. Oil pipeline leak signal image recognition based on improved data field theory. Cluster Comput 22 (Suppl 5), 12949–12957 (2019). https://doi.org/10.1007/s10586-018-1816-9
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DOI: https://doi.org/10.1007/s10586-018-1816-9