Quantitative Identification of Pipeline Crack Based on BP Neural Network

Shu Jun Liu; Sheng Lin Li; Ming Jiang; Dean He

doi:10.4028/www.scientific.net/KEM.737.477

Paper Titles

Damage Detection with FBG Sensors for Pre-Stress Concrete Girders
p.454

Experimental Study of Inner-and-Outer Steel Flanges Subjected to Tension and Bending Loads
p.459

Study on Estimate of Load Ratio of Doubly Reinforced Concrete Beam Exposed to the Standard Fire
p.465

Relaxation of Structural Concrete due to its Shrinkage in Terms of Age-Adjusted Effective Modulus Method
p.471

Quantitative Identification of Pipeline Crack Based on BP Neural Network
p.477

Dynamic Characteristics Analysis of Long-Span Cable-Stayed Bridge with Steel Truss Stiffening Girder
p.481

Air Quenching of Steel Slag to Enhance its Hydraulic Activity for Recycling the Slag as Materials in Cement and Concrete Applications
p.488

Seismic Response Analysis of Reservoir Water-Gravity Dam-Foundation System
p.494

A Stress Analysis of the Post-Tensioned Anchorage Zones Using UHPC
p.500

HomeKey Engineering MaterialsKey Engineering Materials Vol. 737Quantitative Identification of Pipeline Crack...

Quantitative Identification of Pipeline Crack Based on BP Neural Network

Abstract:

In the paper, the Metal Magnetic Memory Testing signal of pipeline crack is extracted. The BP neural network is constructed and trained. The experiment shows that the BP neural network can effectively identify the crack parameters of oil and gas pipeline in quantitative.

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Periodical:

Key Engineering Materials (Volume 737)

Pages:

477-480

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.737.477

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Online since:

June 2017

Authors:

Shu Jun Liu*, Sheng Lin Li, Ming Jiang, Dean He

Keywords:

BP Neural Network, Magnetic Memory Testing, Quantitative Identification, Signal Characteristics, Stress Concentration

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References

[1] A. A. Doubov. A study of metal properties using the method of magnetic memory, Metal Sci. Heat Treatment, 39(9/10) (1997) 401-405.

DOI: 10.1007/bf02469065

Google Scholar

[2] A. A. Doubov. The express technique of welded joints examination with use of metal magnetic memory, NDT & E Int. 33(6) (2000) 351.

Google Scholar

[3] J. L. Ren, G. H. Wu, K. Song, Study on the mechanism of metal magnetic memory testing, Nonde Structive Testing, 24(1) (2002) 29-31.

Google Scholar

[4] J. L. Ren, J. Wang, Z. Z. Fan, New method for metal magnetic memory quantitative analysis, Chinese J. Sci. Instr. 31(2) (2010) 431-436.

Google Scholar

[5] S. J. Shujun, Z. X. Li, Y. Su. Application of Denoising Method Based on Wavelet Packet in Metal Magnetic Memory Testing Signal Processing, Proceedings of the 2010 International Conference on Information Technology and Scientific Management. Tianjin, China: Scientific Research Publishing, USA, (2010).

Google Scholar

[6] B. Duan, T. J. Sun, Z. H. Li. Signal Processing of All-digital Inverter Welder Based on Wavelet, J. Mech. Eng. 46(4) (2010) 60-63.

DOI: 10.3901/jme.2010.04.060

Google Scholar

[7] Y. J. Gao, X. D. Kong, Q. Zhang, Wavelet Packets Analysis Based Method for Hydraulic Pump Condition Monitoring, J. Mech. Eng. 45(8) (2009) 81-83.

Google Scholar

[8] X. B. Luo, T. Q. Chen, Y. Wan. Wavelet Packet T Analysis T0 Signal In Ultrasonic Testing, J. Mech. Eng. 42(4) (2006) 142-143.

Google Scholar

[9] Z. Y. He, S. W. Zhang. Damage diagnosis of structure based on wavelet packet frequency band energy detecting technology, J. Jinan University (Natural Science Edition). 28(5) (2007) 432-433.

Google Scholar

[10] J. Sun, A. Q. Li, Y. L. Ding. Environmental variability of measured wavelet packet energy spectrum for Runyang suspension bridge, J. Southeast University (Natural Science Edition). 39(1) (2009) 91-95.

Google Scholar

[11] Y. L. Ding, A. Q. Li, Y. Deng. Parameters for identification of wavelet packet energy spectrum for structural damage alarming, J. Southeast Univ. (Natural Science Edition). 41(4) (2011) 824-825.

Google Scholar

[12] Y. L. Ding, A. Q. Li, C. Q. Miao, Investigation on the structural damage alarming method based on wavelet packet energy spectrum, Eng. Mech. 23(8) (2006) 42-48.

Google Scholar