Quantitative evaluation of pipe wall thinning defect sizes using microwave NDT

References

• Alleyne DN, Pavlakovic B, Lowe M J S, et al. Rapid, long range inspection of chemical plant pipework using guided waves. AIP Conf Proc. 2001;557(1):180–187.
   Google Scholar
• Yibo L, Liying S, Zhidong S, et al. Study on energy attenuation of ultrasonic guided waves going through girth welds. Ultrasonics. 2006;44:e1111–6.
   PubMed Web of Science ®Google Scholar
• Aristegui C, Lowe MJS, Cawley P. Guided waves in fluid-filled pipes surrounded by different fluids. Ultrasonics. 2001;39(5):367–375.
   Web of Science ®Google Scholar
• Hashizume H, Shibata T, Yuki K. Crack detection method using electromagnetic waves. Int J Appl Electromagnet Mech. 2004;20(3–4):171–178.
   Web of Science ®Google Scholar
• Sasaki K, Katagiri T, Yusa N, et al. Experimental verification of long-range microwave pipe inspection using straight pipes with lengths of 19–26.5 m. NDT E Int. 2018;96:47–57.
   Web of Science ®Google Scholar
• Katagiri T, Chen G, Yusa N, et al. Demonstration of detection of the multiple pipe wall thinning defects using microwaves. Measurement. 2021;175:109074.
   Web of Science ®Google Scholar
• Sasaki K, Katagiri T, Yusa N, et al. Demonstration of the applicability of nondestructive microwave testing to the long-range inspection of inner-surface cracks in tubes. Mater Trans. 2017;58(4):692–696.
   Web of Science ®Google Scholar
• Chen G, Katagiri T, Song H, et al. Detection of cracks with arbitrary orientations in a metal pipe using linearly-polarized circular TE11 mode microwaves. NDT E Int. 2019;107:102125.
   Web of Science ®Google Scholar
• Liu L. Application of microwave for remote NDT and distinction of biofouling and wall thinning defects inside a metal pipe. J Nondestruct Eval. 2015;34(4):1–8.
   Web of Science ®Google Scholar
• Cataldo A, De Benedetto E, Cannazza G, et al. Leak detection through microwave reflectometry: from laboratory to practical implementation. Measurement. 2014;47:963–970.
   Web of Science ®Google Scholar
• Sasaki K, Liu L, Yusa N, et al. Optimized microwave excitation probe for general application in NDT of wall thinning in metal pipes of arbitrary diameter. NDT E Int. 2015;70:53–59.
   Web of Science ®Google Scholar
• Chen G, Katagiri T, Yusa N, et al. Design of a dual-port, side-incident microwave probe for detection of in-pipe damage. Meas Sci Technol. 2020;31(12):125001.
   Web of Science ®Google Scholar
• Sakai Y, Yusa N, Hashizume H. Nondestructive evaluation of wall thinning inside a pipe using the reflection of microwaves with the aid of signal processing. Nondestr Test Eval. 2012;27(2):171–178.
   Web of Science ®Google Scholar
• Cataldo A, De Benedetto E, Angrisani L, et al. A microwave measuring system for detecting and localizing anomalies in metallic pipelines. IEEE Trans Instrum Meas. 2020;70:1–11.
   PubMed Web of Science ®Google Scholar
• Liu L, Ju Y, Chen M. Optimizing the frequency range of microwaves for high-resolution evaluation of wall thinning locations in a long-distance metal pipe. NDT E Int. 2013;57:52–57.
   Web of Science ®Google Scholar
• Sakai Y, Yusa N, Ito S, et al. Numerical analysis of microwave NDT applied to piping inspection. Mater Trans. 2012;53(4):102125–102130.
   Web of Science ®Google Scholar
• Liu L, Ju Y. A high-efficiency nondestructive method for remote detection and quantitative evaluation of pipe wall thinning using microwaves. NDT E Int. 2011;44(1):106–110.
   Web of Science ®Google Scholar
• Saber N, Ju Y, Hsu H, et al. A feasibility study on the application of microwaves for online biofilm monitoring in the pipelines. Int J Press Vessel Pip. 2013;27:99–105.
   Google Scholar
• Pozar DM. Microwave engineering. 4th ed. N.Y: John Wiley & Sons; 2011. p. 125–127.
   Google Scholar
• Harrsion L, Ravan M, Tandel D, et al. Material identification using a microwave sensor array and machine learning. Electronics. 2020;9(2):288.
   Web of Science ®Google Scholar
• Jayalakshmi T, Santhakumaran A. Statistical normalization and back propagation for classification. Int J of Comput Theory Eng. 2011;57(1):89–93.
   Google Scholar
• Zhou Z. Machine learning. Beijing: Tsinghua University Press; 2016. p. 105.
   Google Scholar
• Alobaidi WM, Alkuam EA, Sandgren E. Development of an optimized neural network for the detection of pipe defects using a microwave signal. J Press Vessel Technol. 2018;140(4):041501.
   Web of Science ®Google Scholar