1. Please check and comment entries here.
Table of Contents

    Topic review

    Failure Detection for Pipeline Networks

    Subjects: Engineering, Civil
    View times: 11
    Submitted by: Boon Wong


    Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects.

    1. Introduction

    Pipeline networks are commonly used to transport water, oils and gases over long distances in cities, housing estates and industrial areas. While some pipelines are subject to faults such as weld defects that are caused by a variety of reasons which include poor quality of pipe materials and cracking due to strain [1], most pipelines are openly exposed to environmental conditions, such as rain and floods, and damage due to human error and vandalism, as well as unintended damage due to construction and development activities. For overground pipelines, although structural failures, such as cracks and leakages can be identified visually, often, these failures can only be detected at their critical stages when they become disruptive. For buried, underground and submarine pipelines, where visual inspection is not possible, inspection tools, which are either human-operated [2][3] or automated [4][5], are used.
    Human-operated inspection tools are often inefficient since intensive human participation is required in order to inspect relatively long distances of pipelines daily. Therefore, the employment of automated inspection tools has become increasingly popular. Prior to 2010, growing popularity in the use of ultrasonic-based inspection methods was observed, where research in the optimisation of the geometrical design of ultrasonic-phased arrays for guided wave inspection was actively conducted [6][7][8]. Since 2010, there has been a shift of interest from ultrasonic transducers to acoustic emission (AE) sensing methods [9][10][11]. There has also been a growing interest among researchers in inspection methods based on the analysis of hydraulic parameters such as pressure and flow rate [12][13][14]. At the same time, the employment of magnetic flux leakage (MFL) sensing for pipeline failure detection has become increasingly relevant due to its applicability across various types of pipeline failures [15][16][17].
    Technologies such as ground-penetrating radar (GPR) [2], infrared thermography [18] and impact echo (IE) [19] are widely employed in the industry, especially in human-operated inspection tools. However, the dimensions, designs and operational requirements of the sensing devices for these technologies have constrained them from being adopted in remote and automated pipeline monitoring systems [20]. In conjunction with the extensive implementation of Industry 4.0 [21][22][23], sensors, such as ultrasonic, acoustic, hydraulic and Hall effect sensors, have been retrofitted in the form of wireless sensor networks in existing pipeline networks [24][25][26]. These sensors are often small in size, inexpensive and can be easily interfaced with embedded systems. These technologies became increasingly relevant with the deployment of autonomous robots for the direct measurement of the magnitudes of defects in pipeline networks [27][28]. The emergence of unmanned aerial vehicles, better known as drones, for the detection of surface defects of pipelines has overcome the limitations of remote monitoring, at the same time reducing the workload required to monitor the integrity of pipelines in large plants [3].
    Since 2010, many researchers have been focusing on developing efficient pre-processing and pipeline failure categorisation techniques for data or signals collected from sensors by employing machine learning methods [29][30] suited to embedded platforms. The pre-processing of data or signals using such methods as Kalman filter [13] and wavelet transform algorithms [31] helps to increase the reliability of failure categorisation techniques through the removal of noise and the enhancement of quality. There has also been an increasing interest in the image reconstruction of the in-pipe environment, where detection methods, such as process tomography [32][33], are becoming more mainstream. As of today, many innovative wireless sensor networks for pipeline systems emphasize the failure response time, efficiency of computation and the reliability of the communication systems used [14][34][35][36][37][38][39][40]. By having a combination of physical pipeline networks, sensing capabilities and computational elements, wireless sensor networks for the detection of defects in pipelines are essentially part of the family of cyber-physical systems.

    2. Pipeline Failure Detection Methods

    A failure or defect in a pipeline can exist generally in the form of a crack, a blockage, a leakage, a weld defect or corrosion. Cracks and leakages in pipelines may be caused by mechanical stress, pressure and prolonged thinning of the pipeline due to corrosion. Blockages in pipelines are normally caused by oversized loads or the build-up of sediments. Corrosion, which is related to the ageing of pipelines, is induced by the oxidation of the metallic wall of the pipeline and friction between the transported load and the inner wall of the pipeline, as well as the corrosive nature of the load. Weld defects at pipeline joints are attributed to poor welding jobs and mechanical damage due to fluid pressure and ambient stress. In order to avoid the occurrence of disruptive failures, the early detection of pipeline defects is necessary [41].
    Table 1, in the form of a look-up table, shows various existing non-destructive methods along with their suitability for the detection of different pipeline defects. The key aspects and common data or signal processing techniques for each of the methods are also enumerated in the same table. The failure detection methods covered in this paper are non-exhaustive and are, to the best of our knowledge at the point of writing, include non-destructive technologies that have been practically validated in the industry either in the form of modern wireless sensor networks or human-operated devices.
    Table 1. The suitability of existing non-destructive methods for the detection of different pipeline defects.
    Failure Detection Methods Defect Type Key Aspects/Data or Signal Processing Techniques References
    Blockage Leakage Crack Corrosion Weld Defect
    Acoustic Reflectometry Time-of-flight; phase change; power reflection ratio; spectral analysis; synthetic aperture radar; acoustic resonance technology; ultrasonic phased array [6][7][8][42][43][44][45][46]
    Guided Wave Inspection   Time-of-flight; ultrasonic transducer ring; phase change; spectral analysis; transmission/reflection coefficient analysis; non-linear modulation; guided microwave inspection [9][47][48][49][50][51][52][53][54]
    Ultrasonic Gauging       Time-of-flight; time-series cross-correlation; Gaussian model-based estimation; temperature compensation [55][56][57][58][59]
    Ground Penetrating RaDAR (GPR)         Back-projection; back-propagation; GPR-camera fusion; Bayern approximation [2][60][61][62][63][64][65]
    Impact Echo (IE)       Sustained duration; resonance analysis; correction factor validation; Edge reflection analysis; noise removal [19][66][67][68][69][70]
    Acoustic Emission (AE)/ Vibration Analysis   Frequency analysis; vibrational amplitude and fluid transient analysis; time-difference cross-correlation; wavelet entropy analysis; machine learning classification [1][9][10][11][30][38][71][72]
    Resonance Shift Analysis     System resonant frequency, amplitude, quality factor and bandwidth shifts analysis [73][74][75][76][77][78]
    Hydraulic Transient Analysis       Finite difference modelling; linear estimator; short duration transient test; fluid transient harmonic damping analysis; negative pressure method; gradient method; sequential probability ratio technique; wavelet transforms [12][13][31][79][80][81][82][83][84][85][86]
    Micro-Electro-Mechanical System (MEMS)         Piezoelectric sensors; capacitive sensors [87][88][89][90][91][92][93][94]
    Magnetic Flux Leakage (MFL)     Amplitude of MFL vs. length/width of defect; machine learning classification; decoupling algorithm [4][16][17][95][96][97][98][99][100][101][102]
    Pulsed Eddy Current (PEC)         Electrical conductance analysis; magnetic permeability analysis; differential probe [103][104][105][106][107]
    Fibre Optic Sensing     Spectral analysis; hoop strain analysis [5][108][109][110][111][112][113][114][115][116]
    Mobile Sensing/Robots/Drones   Pressure gradient analysis; pipeline inspection gauge (PIG); driving mechanisms; manoeuvrability [3][4][27][28][95][108][117][118][119][120][121][122]
    Process Tomography   Electrical capacitance measurement; magnetic induction measurement; ultrasonic measurement; image reconstruction; linear back-projection; narrow-band pass filtering [32][33][123][124][125]
    Radiography       Pixel intensity vs. pipe thickness; double wall double image technique; machine learning classification [126][127][128][129]
    Infrared Thermography       Thermal emissivity; thermal capacity; pulsed thermography; step heating thermography; lock-in thermography; spectral analysis, [3][18][130][131][132]
    Optical Inspection   Light intensity of image vs. surface condition/texture [133][134][135][136]
    Gamma-ray Transmission         Transmission intensity vs. pipe thickness [137]
    Vapour Sampling         Vapour sensing tube [138]
    Fluorescence         Wavelength of fluorescence vs. type of spillage [139][140][141]
    Electromechanical Impedance (EMI)       EMI vs. structural integrity; piezoelectric-induced vibration; measurement of electrical impedance [142][143][144][145][146][147][148]
    Electrochemical Impedance Spectroscopy (EIS)         Impedance measurement; polarisation resistance vs. corrosion rate [149][150]
    Corrosion Growth Modelling         Stochastic corrosion model; Monte Carlo simulation [151][152][153][154][155]
    Distributed Cyber-physical Systems         Wireless sensor networks; pressure and acoustic data analysis; post-order transversal algorithm; WaterBox; search algorithm; machine learning [25][34][35][40][156][157][158][159][160]

    The entry is from 10.3390/s21154959


    1. Droubi, M.G.; Faisal, N.H.; Orr, F.; Steel, J.A.; El-Shaib, M. Acoustic emission method for defect detection and identification in carbon steel welded joints. J. Constr. Steel Res. 2017, 134, 28–37.
    2. Demirci, S.; Yigit, E.; Eskidemir, I.H.; Ozdemir, C. NDT & E International Ground penetrating radar imaging of water leaks from buried pipes based on back-projection method. NDT&E Int. 2012, 47, 35–42.
    3. Shakmak, B.; Al-Habaibeh, A. Detection of water leakage in buried pipes using infrared technology; A comparative study of using high and low resolution infrared cameras for evaluating distant remote detection. In Proceedings of the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2015, Amman, Jordan, 3–5 November 2015.
    4. Kim, H.M.; Yoo, H.R.; Rho, Y.W.; Park, G.S. Detection method of cracks by using magnetic fields in underground pipeline. In Proceedings of the 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2013, Jeju, Korea, 30 October–2 November 2013; pp. 734–737.
    5. Ren, L.; Jiang, T.; Jia, Z.; Li, D.; Yuan, C.; Li, H. Pipeline corrosion and leakage monitoring based on the distributed optical fi ber sensing technology. Measurement 2018, 122, 57–65.
    6. Du, Y.H.; Jin, S.J. Synthetic aperture beamformer for pipeline girth weld inspection. In Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling (KAM), KAM 2008, Wuhan, China, 21–22 December 2008; Volume 2, pp. 407–409.
    7. Li, Z. Modelling and Simulation of Ultrasonic Phased Array in Pipe Flaw Detection. Int. Conf. Innov. Comput. Inf. Control Vol. I 2006, 3, 145–148.
    8. Zhan, X.; Zhou, D.; Chen, S.; Jin, S. Research on automatic flaw detection of pipeline girth weld by ultrasonic phased array system. In Proceedings of the 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009, Changchun, China, 9–12 August 2009; pp. 4310–4315.
    9. Martins, J.C.; Seleghim, P. Assessment of the Performance of Acoustic and Mass Balance Methods for Leak Detection in Pipelines for Transporting Liquids. J. Fluids Eng. 2010, 132, 011401.
    10. Ye, Y.; Zhang, L.; Liang, W. Study on leakage acoustic signal in natural gas pipeline. In Proceedings of the 4th International Conference on Computational and Information Sciences, ICCIS 2012, Chongqing China, 17–19 August 2012; pp. 1244–1247.
    11. Huang, J.; Chen, G.; Shu, L.; Chen, Y.; Zhang, Y. Impact of Fouling on Flow-Induced Vibration Characteristics in Fluid-Conveying Pipelines. IEEE Access 2016, 4, 6631–6644.
    12. Li, J.; Liu, W.; Sun, Z.; Cui, L. A new failure detection method and its application in leak monitor of pipeline. Int. Conf. Control. Autom. Robot. 2008, 10, 1178–1182.
    13. Zhang, Y.; Li, J.; Zeng, Z.; Shijiu, J. A combined Kalman filter—Discrete wavelet transform method for leakage detection of crude oil pipelines. In Proceedings of the 9th International Conference on Electronic Measurement and Instruments (ICEMI 2009), Beijing, China, 16–19 August 2009; pp. 31086–31090.
    14. Saeed, H.; Ali, S.; Rashid, S.; Qaisar, S.; Felemban, E. Reliable monitoring of oil and gas pipelines using wireless sensor network (WSN)—REMONG. In Proceedings of the 9th International Conference on System of Systems Engineering: The Socio-Technical Perspective, SoSE 2014, Adelaide, Australia, 9–13 June 2014; pp. 230–235.
    15. Joshi, A.; Udpa, L.; Udpa, S.; Tamburrino, A. Adaptive Wavelets for Characterizing Magnetic Flux Leakage Signals From Pipeline Inspection. IEEE Int. Magn. Conf. 2006, 42, 3168–3170.
    16. Zhang, G.; Liu, J. Finite element modelling of circumferential magnetic flux leakage inspection in pipeline. Int. Conf. Intell. Comput. Technol. Autom. 2010, 2, 327–330.
    17. Pasha, M.A.; Khan, T.M. A pipeline inspection gauge based on low cost magnetic flux leakage sensing magnetometers for non-destructive testing of pipelines. In Proceedings of the 2016 International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies, IEEE EmergiTech 2016, Port Louis, Mauritius, 1–6 August 2016; pp. 1–5.
    18. Doshvarpassand, S.; Wu, C.; Wang, X. Infrared Physics & Technology An overview of corrosion defect characterization using active infrared thermography. Infrared Phys. Technol. 2019, 96, 366–389.
    19. Mo, J.; Song, S.; Park, D.; Choi, C. Detection of cavities around concrete sewage pipelines using impact-echo method. Tunn. Undergr. Space Technol. Inc. Trenchless Technol. Res. 2017, 65, 1–11.
    20. Datta, S.; Sarkar, S. Journal of Loss Prevention in the Process Industries A review on different pipeline fault detection methods. J. Loss Prev. Process Ind. 2016, 41, 97–106.
    21. Babazadeh, M.; Kartakis, S.; McCann, J.A. Highly-distributed sensor processing using IoT for critical infrastructure monitoring. In Proceedings of the 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017, Sapporo, Japan, 12–15 December 2018; pp. 1065–1074.
    22. Kolcun, R.; Boyle, D.; Mccann, J.A. Efficient In-Network Processing for a Hardware-Heterogeneous IoT. In Proceedings of the 6th International Conference on the Internet of Things, IoT’16, Stuttgart, Germany, 7–9 November 2016; pp. 93–101.
    23. Dobaj, J.; Iber, J.; Krisper, M.; Kreiner, C. A Microservice Architecture for the Industrial Internet-Of-Things. In Proceedings of the 23rd European Conference on Pattern Languages of Programs—EuroPLoP ’18, Irsee, Germany, 4–8 July 2018; pp. 1–15.
    24. Kiziroglou, M.E.; Boyle, D.E.; Wright, S.W.; Yeatman, E.M. Acoustic power delivery to pipeline monitoring wireless sensors. Ultrasonics 2017, 77, 54–60.
    25. Ayadi, A.; Ghorbel, O.; Obeid, A.; Bensaleh, M.S.; Abid, M. Leak detection in water pipeline by means of pressure measurements for WSN. In Proceedings of the 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017, Fez, Morocco, 22–24 May 2017; pp. 1–6.
    26. Sun, Z.; Wang, P.; Vuran, M.C.; Al-rodhaan, M.A.; Al-dhelaan, A.M.; Akyildiz, I.F. Ad Hoc Networks MISE-PIPE: Magnetic induction-based wireless sensor networks for underground pipeline monitoring. Ad Hoc Netw. 2011, 9, 218–227.
    27. Chatzigeorgiou, D.; Wu, Y.; Youcef-Toumi, K.; Ben-Mansour, R. MIT Leak Detector: An in-pipe leak detection robot. IEEE Int. Conf. Robot. Autom. 2014, 2091.
    28. Lai, T.T.T.; Chen, W.J.; Chen, Y.H.T.; Huang, P.; Chu, H.H. Mapping hidden water pipelines using a mobile sensor droplet. ACM Trans. Sens. Netw. 2013, 9, 1–33.
    29. Fahad, M.; Kamal, K.; Zafar, T.; Qayyum, R.; Tariq, S.; Khan, K. Corrosion detection in industrial pipes using guided acoustics and radial basis function neural network. In Proceedings of the International Conference on Robotics and Automation Sciences, ICRAS 2017, Hong Kong, China, 26–29 August 2017; pp. 129–133.
    30. Wang, Q.; Yuan, C.; Zhu, J. Buried pipeline third-party damage signals classification based on LS-SVM. Proc. World Congr. Intell. Control Autom. 2006, 1, 5032–5036.
    31. Liu, J.; Li, X.; Zhang, H.; Liu, D. Noise reduction for oil pipeline pressure time series based on wavelet filtering technology. In Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009, Shenyang, China, 5–7 August 2009; pp. 1507–1512.
    32. Li, N.; Liu, K.; Yang, X.; Cao, M. Research on Application of Wax Deposition Detection in the Nonmetallic Pipeline Based on Electrical Capacitance Tomography. J. Sens. 2016, 2016.
    33. Evangelidis, M.; Ma, L.; Soleimani, M. Pipeline inspection using high resolution electrical capacitance tomography. In Proceedings of the 7th World Congress in Industrial Process Tomography, Krakow, Poland, 2–5 September 2013; pp. 556–561.
    34. Stoianov, I.; Nachman, L.; Madden, S. PIPENET: A Wireless Sensor Network for Pipeline Monitoring. Inf. Process. Sens. Netw. 2007.
    35. Yu, W.; Mccann, J.A. Effectively Positioning Water Loss Event in Smart Water Networks. In Proceedings of the 2nd International Electronic Conference on Sensors and Applications, e-Conference, 15–30 November 2015.
    36. Kartakis, S.; Yu, W.; Akhavan, R.; McCann, J.A. Adaptive edge analytics for distributed networked control of water systems. In Proceedings of the 2016 IEEE 1st International Conference on Internet-of-Things Design and Implementation, IoTDI 2016, Berlin, Germany, 4–8 April 2016; pp. 72–82.
    37. Kartakis, S.; Yang, S.; Mccann, J.A. Reliability or Sustainability: Optimal Data Stream Estimation and Scheduling in Smart Water Networks. ACM Trans. Sens. Netw. 2017, 13, 1–27.
    38. Lile, N.L.T.; Jaafar, M.H.M.; Roslan, M.R.; Azmi, M.S.M. Blockage Detection in Circular Pipe Using Vibration Analysis. Int. J. Adv. Sci. Eng. Inf. Technol. 2012, 2, 54–57.
    39. Whittle, A.J.; Girod, L.; Preis, A.; Allen, M.; Lim, H.; Iqbal, M.; Srirangarajan, S.; Fu, C.; Wong, K.J.; Goldsmith, D. Waterwise @ SG: A testbed for continuous monitoring of the water distribution system in singapore. In Proceedings of the 12th Annual Conference on Water Distribution Systems Analysis (WDSA), Tucson, AZ, USA, 12–15 September 2010.
    40. Kartakis, S.; Abraham, E.; McCann, J.A. WaterBox: A Testbed for Monitoring and Controlling Smart Water Networks. In Proceedings of the 1st ACM International Workshop Cyber-Physical Systems Smart Water Networks (CySWater), Seattle, WA, USA, 14–16 April 2015; Volume 1, pp. 1–6.
    41. Rezaei, H.; Ryan, B.; Stoianov, I. Pipe failure analysis and impact of dynamic hydraulic conditions in water supply networks. Procedia Eng. 2015, 119, 253–262.
    42. Vidal, J.E.; Silva, L.; Netto, T.; Monteiro, P.C.C. Acoustic Reflectometry For Blockages Detection In Pipeline. OTC Brasil 2013, 3916–3923.
    43. Duan, W.; Kirby, R.; Prisutova, J.; Horoshenkov, K.V. On the use of power reflection ratio and phase change to determine the geometry of a blockage in a pipe. Appl. Acoust. 2015, 87, 190–197.
    44. Papadopoulou, K.A.; Shamout, M.N.; Lennox, B.; Mackay, D.; Taylor, A.R.; Turner, J.T.; Wang, X. An evaluation of acoustic reflectometry for leakage and blockage detection. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2008, 222, 959–966.
    45. Zhan, X.; Li, J.; Jin, S. Research on ultrasonic phased array system for automatic defect detection of pipeline girth welds. World Congr. Intell. Control. Autom. 2010, 5454–5459.
    46. Vos, W.; As, H. Using Acoustic Resonance for the In Line Inspection of Pipelines. In Proceedings of the Pipeline Technology Conference, Berlin, Germany, 2–4 May 2017.
    47. Ma, J.; Lowe, M.J.S.; Simonetti, F. Feasibility study of blockage detection inside pipes using guided ultrasonic waves. In Proceedings of the 17th World Conference on Non Destructive Testing (17th WCNDT), Shanghai, China, 25–28 October 2008.
    48. Leinov, E.; Lowe, M.J.; Cawley, P. Investigation of guided wave propagation and attenuation in pipe buried in sand. J. Sound Vib. 2015, 347, 96–114.
    49. Shoupengi, S.; Peiwen, Q.; Qingkun, L. Wavelet-based pipe flaw 2D-reconstruction scheme using line-focusing ultrasonic transducer array. In Proceedings of the 2006 15th IEEE International Symposium on the Applications of Ferroelectrics, Sunset Beach, NC, USA, 30 July–3 August 2006.
    50. Khalili, P.; Cawley, P. NDT and E International The choice of ultrasonic inspection method for the detection of corrosion at inaccessible locations. NDT&E Int. 2018, 99, 80–92.
    51. Lu, Y.; Liu, L.S.; Ishikawa, M. Quantitative Evaluation of Wall Thinning of Metal Pipes by Microwaves. Mater. Sci. Forum 2009, 614, 111–116.
    52. Jhang, K.Y. Erratum to: Nonlinear ultrasonic techniques for nondestructive assessment of micro damage in material: A review (International Journal of Precision Engineering and Manufacturing, (2009), 10, 1, (123-135), 10.1007/s12541-009-0019-y). Int. J. Precis. Eng. Manuf. 2017, 18, 139.
    53. Li, N.; Sun, J.; Jiao, J.; Wu, B.; He, C. Quantitative evaluation of micro-cracks using nonlinear ultrasonic modulation method. NDT&E Int. 2016, 79, 63–72.
    54. Jiao, J.; Sun, J.; Li, N.; Song, G.; Wu, B.; He, C. Micro-crack detection using a collinear wave mixing technique. NDT&E Int. 2014, 62, 122–129.
    55. Honarvar, F.; Salehi, F.; Safavi, V.; Mokhtari, A.; Sinclair, A.N. Ultrasonic monitoring of erosion/corrosion thinning rates in industrial piping systems. Ultrasonics 2013, 53, 1251–1258.
    56. Adamowski, J.C.; Buiochi, F.; Tsuzuki, M.; Perez, N.; Camerini, C.S.; Patusco, C. Ultrasonic measurement of micrometric wall-thickness loss due to corrosion inside pipes. IEEE Int. Ultrason. Symp. 2013.
    57. Javadi, Y.; Pirzaman, H.S.; Raeisi, M.H.; Najafabadi, M.A. Ultrasonic inspection of a welded stainless steel pipe to evaluate residual stresses through thickness. Mater. Des. 2013, 49, 591–601.
    58. Waag, G.; Hoff, L.; Norli, P. Air-coupled ultrasonic through-transmission thickness measurements of steel plates. Ultrasonics 2015, 56, 332–339.
    59. Cheong, Y.M.; Kim, K.M.; Kim, D.J. High-temperature ultrasonic thickness monitoring for pipe thinning in a flow-accelerated corrosion proof test facility. Nucl. Eng. Technol. 2017, 49, 1463–1471.
    60. Jol, H.M.; Smith, D.G. Ground penetrating radar surveys of peatlands for oilfield pipelines in Canada. J. Appl. Geophys. 1995, 34, 109–123.
    61. Bimpas, M.; Amditis, A.; Uzunoglu, N.K. Design and Implementation of an Integrated High Resolution Imaging Ground Penetrating Radar for Water Pipeline Rehabilitation. Water Resour. Manag. 2011, 25, 1239–1250.
    62. Lähivaara, T.; Dudley Ward, N.F.; Huttunen, T.; Kaipio, J.P.; Niinimäki, K. Estimating pipeline location using ground-penetrating radar data in the presence of model uncertainties. Inverse Probl. 2014, 30, 114006.
    63. Yang, H.W.; Yang, Z.K.; Pei, Y.K. Ground-penetrating radar for soil and underground pipelines using the forward modeling simulation method. Optik 2014, 125, 7075–7079.
    64. Travassos, X.L.; Avila, S.L.; Ida, N. Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review. Appl. Comput. Inform. 2018.
    65. Li, H.; Chou, C.; Fan, L.; Li, B.; Wang, D.; Song, D. Toward Automatic Subsurface Pipeline Mapping by Fusing a Ground-Penetrating Radar and a Camera. IEEE Trans. Autom. Sci. Eng. 2020, 17, 722–734.
    66. Wouters, J.; Poston, R.W. Applications of impact-echo for flaw detection. Struct. A Struct. Eng. Odyssey Proc. 2001 Struct. Congr. Expo. 2004, 109, 1–10.
    67. Kommireddi, C.R.; Gassman, S.L. Impact echo evaluation of thin walled concrete pipes. In Proceedings of the ASCE Pipeline Division Specialty Congress—Pipeline Engineering and Construction, San Diego, CA, USA, 1–4 August 2004; pp. 291–300.
    68. Gibson, A.; Popovics, J.S. Lamb Wave Basis for Impact-Echo Method Analysis. J. Eng. Mech. 2005, 131, 438–443.
    69. Sivasubramanian, K.; Jaya, K.P.; Neelamegam, M. Virtual Edge Extension Technique to Reduce the Edge Effect in Impact-Echo Method. J. Perform. Constr. Facil. 2016, 30, 04014205.
    70. Gómez, P.; Fernández-Álvarez, J.P.; Áres, A.; Fernández, E. Guided-Wave Approach for Spectral Peaks Characterization of Impact-Echo Tests in Layered Systems. J. Infrastruct. Syst. 2017, 23, 04017009.
    71. Yan, J.; Feng, Z.; Wu, J.; Ma, J. Research on identifying drainage pipeline blockage based on multi-feature fusion. In Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, Chongqing, China, 28–30 May 2017; pp. 4193–4198.
    72. Tang, X.; Liu, Y.; Zheng, L.; Ma, C.; Wang, H. Leak detection of water pipeline using wavelet transform method. Int. Conf. Environ. Sci. Inf. Appl. Technol. 2009, 2, 217–220.
    73. Duan, H.; Lee, P.J.; Kashima, A.; Ghidaoui, M.S. Extended Blockage Detection in Pipes Using the System Frequency Response: Analytical Analysis and Experimental Verification. J. Hydraul. Eng. 2013, 2014.
    74. Duan, H.F.; Lee, P.J.; Ghidaoui, M.S.; Tuck, J. Transient wave-blockage interaction and extended blockage detection in elastic water pipelines. J. Fluids Struct. 2014, 46, 2–16.
    75. Nishkala, K.; Royan, B.T.; Aishwarya, H.M.; V, S.D.R.; Kurup, D.G. Detection of Ruptures in Pipeline Coatings Using Split Ring Resonator Sensor. In Proceedings of the 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 19–22 September 2018; pp. 1646–1649.
    76. Lai, C.; Xu, W.; Sun, X. Development of an inverse algorithm for resonance inspection. J. Vib. Acoust. Trans. ASME 2012, 134, 1–10.
    77. Che, T.C.; Duan, H.F.; Pan, B.; Lee, P.J.; Ghidaoui, M.S. Energy Analysis of the Resonant Frequency Shift Pattern Induced by Nonuniform Blockages in Pressurized Water Pipes. J. Hydraul. Eng. 2019, 145, 04019027.
    78. Saber, N.; Ju, Y.; Hsu, H.Y.; Lee, S.H. A feasibility study on the application of microwaves for online biofilm monitoring in the pipelines. Int. J. Press. Vessel. Pip. 2013, 111–112, 99–105.
    79. Scola, I.R.; Besancon, G.; Georges, D. Blockage location in pipelines using an implicit nonlinear finite-difference model optimization. IFAC-PapersOnLine 2018, 51, 935–940.
    80. Massari, C.; Yeh, T.C.; Ferrante, M.; Brunone, B.; Meniconi, S. A stochastic tool for determining the presence of partial blockages in viscoelastic pipelines: First experimental results. Procedia Eng. 2014, 70, 1112–1120.
    81. Wang, X.J.; Lambert, M.F.; Simpson, A.R. Detection and Location of a Partial Blockage in a Pipeline Using Damping of Fluid Transients. J. Water Resour. Plan. Manag. 2005, 131, 244–249.
    82. Ostapkowicz, P. Leak detection in liquid transmission pipelines using simplified pressure analysis techniques employing a minimum of standard and non-standard measuring devices. Eng. Struct. 2016, 113, 194–205.
    83. Lu, S.; Liu, Z.; Li, S. Multi-points synchronous measurement of pressure used in burst and leakage monitoring along the water transmission pipeline. In Proceedings of the 2011 2nd International Conference on Mechanic Automation and Control Engineering, MACE 2011, Inner Mongolia, China, 15–17 July 2011; pp. 2312–2317.
    84. Chen, Z.; Lian, X.; Yu, Z. Leakage Detection for Oil Pipelines Based on Independent Component Analysis. In Proceedings of the 29th Chinese Control Conference, Beijing, China, 29–31 July 2010.
    85. Shi, Y.; Wang, Z. Detection of small leakage from pipeline based on improved harmonic wavelet. In Proceedings of the ICCSE 2012—Proceedings of 2012 7th International Conference on Computer Science and Education, Melbourne, Australia, 14–17 July 2012.
    86. Kim, S. Inverse transient analysis for a branched pipeline system with leakage and blockage using impedance method. Procedia Eng. 2014, 89, 1350–1357.
    87. Berjaoui, S.; Alkhatib, R.; Elshiekh, A.; Morad, M.; Diab, M.O. Free flowing robot for automatic pipeline leak detection using piezoelectric film sensors. In Proceedings of the Mediterranean Gas and Oil International Conference, MedGO 2015—Conference Proceedings, Beirut, Lebanon, 16–18 April 2015; pp. 1–3.
    88. Shinozuka, M.; Chou, P.H.; Kim, S.; Kim, R.; Yoon, E.; Shinozuka, M.; Chou, P.H.; Kim, S.; Kim, H.R.; Mustafa, H.; et al. Nondestructive monitoring of a pipe network using a MEMS-based wireless network. Proc. SPIE 2010, 2010.
    89. Chen, Q.; Zhang, Q.; Niu, X.; Wang, Y. Positioning Accuracy of a Pipeline Surveying System Based on MEMS IMU and Odometer: Case Study. IEEE Access 2019, 7, 104453–104461.
    90. Guan, L.; Cong, X.; Sun, Y.; Gao, Y.; Iqbal, U.; Noureldin, A. Enhanced MEMS SINS Aided Pipeline Surveying System by Pipeline Junction Detection in Small Diameter Pipeline. IFAC-PapersOnLine 2017, 50, 3560–3565.
    91. Xu, J.; Chai, K.T.C.; Wu, G.; Han, B.; Wai, E.L.C.; Li, W.; Yeo, J.; Nijhof, E.; Gu, Y. Low-cost, tiny-sized MEMS hydrophone sensor for water pipeline leak detection. IEEE Trans. Ind. Electron. 2019, 66, 6374–6382.
    92. Nguyen, S.D.; Paprotny, I.; Wright, P.K.; White, R.M. In-plane capacitive MEMS flow sensor for low-cost metering of flow velocity in natural gas pipelines. In Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS), San Francisco, CA, USA, 26–30 January 2014; pp. 971–974.
    93. Song, X.; Jian, Z.; Zhang, G.; Liu, M.; Guo, N.; Zhang, W. New research on MEMS acoustic vector sensors used in pipeline ground markers. Sensors 2015, 15, 274–284.
    94. Nguyen, S.D.; Paprotny, I.; Wright, P.K.; White, R.M. MEMS capacitive flow sensor for natural gas pipelines. Sens. Actuators A Phys. 2015, 231, 28–34.
    95. Kim, H.M.; Rho, Y.W.; Yoo, H.R.; Cho, S.H.; Kim, D.K.; Koo, S.J.; Park, G.S. A study on the measurement of axial cracks in the Magnetic Flux Leakage NDT system. In Proceedings of the IEEE International Conference on Automation Science and Engineering, Seoul, Korea, 20–24 August 2012; pp. 624–629.
    96. Perez Blanco, I.C.; Panqueva Alvarez, J.H.; Dobmann, G. Simulation for magnetic flux leakage signal interpretation: A FE-approach to support in-line magnetic pipeline pigging. In Proceedings of the FENDT 2014—Proceedings, 2014 IEEE Far East Forum on Nondestructive Evaluation/Testing: New Technology and Application, Increasingly Perfect NDT/E, Chengdu, China, 20–23 June 2014; pp. 349–353.
    97. Zhang, G.; Li, P. Signal processing technology of circumferential magnetic flux leakage inspection in pipeline. Proc. Int. Conf. Meas. Technol. Mechatronics Autom. 2011, 3, 229–232.
    98. Liu, D.; Luan, X.; Zhang, F.; Jin, J.; Guo, J.; Zheng, R. An USV-based laser fluorosensor for oil spill detection. In Proceedings of the 10th International Conference on Sensing Technology (ICST), Nanjing, China, 11–13 November 2016.
    99. Kim, H.M.; Heo, C.G.; Cho, S.H.; Park, G.S. Determination scheme for accurate defect depth in underground pipeline inspection by using magnetic flux leakage sensors. IEEE Trans. Magn. 2018, 54, 1–5.
    100. Liying, S.U.N.; Yibo, L.I.; Libo, S.U.N.; Lingge, L.I. Comparison of Magnetic Flux Leakage ( MFL ) and Acoustic Emission ( AE ) Techniques in corrosion Inspection for Pressure Pipelines. In Proceedings of the 31st Chinese Conference, Hefei, China, 25–27 July 2012; pp. 5375–5378.
    101. Kim, H.M.; Park, G.S. A New Sensitive Excitation Technique in Nondestructive Inspection for Underground Pipelines by Using Differential Coils. IEEE Trans. Magn. 2017, 53.
    102. Gloria, N.B.S.; Areiza, M.C.L.; Miranda, I.V.J.; Rebello, J.M.A. ARTICLE IN PRESS NDT & E International Development of a magnetic sensor for detection and sizing of internal pipeline corrosion defects. NDT&E Int. 2009, 42, 669–677.
    103. Ulapane, N.; Alempijevic, A.; Vidal Calleja, T.; Miro, J.V. Pulsed eddy current sensing for critical pipe condition assessment. Sensors 2017, 17, 2208.
    104. Safizadeh, M.; Hasanian, M. Gas Pipeline Corrosion Mapping Using Pulsed Eddy Current Technique. Adv. Des. Manuf. Technol. 2011, 5, 11–18.
    105. Angani, C.S.; Park, D.G.; Kim, C.G.; Leela, P.; Kollu, P.; Cheong, Y.M. The pulsed eddy current differential probe to detect a thickness variation in an insulated stainless steel. J. Nondestruct. Eval. 2010, 29, 248–252.
    106. Piao, G.; Guo, J.; Hu, T.; Deng, Y.; Leung, H. A novel pulsed eddy current method for high-speed pipeline inline inspection. Sens. Actuators A Phys. 2019, 295, 244–258.
    107. Park, D.G.; Angani, C.S.; Kim, G.D.; Kim, C.G.; Cheong, Y.M. Evaluation of pulsed eddy current response and detection of the thickness variation in the stainless steel. IEEE Trans. Magn. 2009, 45, 3893–3896.
    108. Wang, Z.; Cao, Q.; Luan, N.; Zhan, L. Development of an autonomous in-pipe robot for offshore pipeline maintenance. Ind. Robot 2010, 37, 177–184.
    109. Hou, Q.; Jiao, W.; Zhan, S.; Ren, L.; Jia, Z. Natural gas pipeline leakage detection based on FBG strain sensor. In Proceedings of the 2013 5th Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2013, Hong Kong, China, 16–17 January 2013.
    110. Han, B.; Li, L.; Wu, Z.; Jing, H. Applications of FBG and ZigBee in telemetering of vortex-induced vibration for pipelines. In Proceedings of the 2013 Wireless and Optical Communications Conference, WOCC 2013, Chongqing, China, 16–18 May 2013; pp. 595–600.
    111. Bhuiyan, M.A.S.; Hossain, M.A.; Alam, J.M. A computational model of thermal monitoring at a leakage in pipelines. Int. J. Heat Mass Transf. 2016, 92, 330–338.
    112. Amanzadeh, M.; Aminossadati, S.M.; Kizil, M.S.; Rakić, A.D. Recent developments in fibre optic shape sensing. Meas. J. Int. Meas. Confed. 2018, 128, 119–137.
    113. Poletti, F.; Wheeler, N.V.; Petrovich, M.N.; Baddela, N.; Numkam Fokoua, E.; Hayes, J.R.; Gray, D.R.; Li, Z.; Slavík, R.; Richardson, D.J. Towards high-capacity fibre-optic communications at the speed of light in vacuum. Nat. Photonics 2013, 7, 279–284.
    114. Schenato, L. A review of distributed fibre optic sensors for geo-hydrological applications. Appl. Sci. 2017, 2017, 896.
    115. Wong, L.; Rathnayaka, S.; Chiu, W.K.; Kodikara, J. Fatigue Damage Monitoring of a Cast Iron Pipeline Using Distributed Optical Fibre Sensors. Procedia Eng. 2017, 188, 293–300.
    116. Campanella, C.E.; Cuccovillo, A.; Campanella, C.; Yurt, A.; Passaro, V.M. Fibre Bragg Grating based strain sensors: Review of technology and applications. Sensors 2018, 18, 3115.
    117. Dimitris, M.; Wu, Y.; Youcef-toumi, K.; Control, N.; Systems, D.; Modeling, D.; Detection, F.; Systems, T.; Motion, H.; Systems, A.; et al. Reliable Sensing of Leaks in Pipelines. In Proceedings of the ASME 2013 Dynamic Systems and Control Conference, ASME, Palo Alto, CA, USA, 21–23 October 2013.
    118. Gargade, A.A.; Ohol, S.S. Development of Actively Steerable In-pipe Inspection Robot for Various Sizes. In Proceedings of the Advances in Robotics on—AIR’17, New Delhi, India, 28 June–2 July 2017; pp. 1–5.
    119. Harish, P.; Venkateswarlu, V. Design and Motion Planning of Indoor Pipeline Inspection Robot. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2013, 3, 41–47.
    120. Kakogawa, A.; Nishimura, T.; Ma, S. Development of a screw drive in-pipe robot for passing through bent and branch pipes. In Proceedings of the 2013 44th International Symposium on Robotics, ISR 2013, Seoul, Korea, 24–26 October 2013; pp. 1–6.
    121. Nayak, A.; Pradhan, S.K. Design of a new in-pipe inspection robot. Procedia Eng. 2014, 97, 2081–2091.
    122. Mazraeh, A.A.; Ismail, F.B.; Khaksar, W.; Sahari, K. Development of Ultrasonic Crack Detection System on Multi-diameter PIG Robots. Procedia Comput. Sci. 2017, 105, 282–288.
    123. Yao, J.; Takei, M. Application of Process Tomography to Multiphase Flow Measurement in Industrial and Biomedical Fields: A Review. IEEE Sens. J. 2017, 17, 8196–8205.
    124. Ma, L.; Soleimani, M. Evaluation of magnetic induction tomography for pipeline inspection. In Proceedings of the 7th World Congress in Industrial Process Tomography, Krakow, Poland, 2–5 September 2013; pp. 786–791.
    125. Nordin, N.; Idroas, M.; Zakaria, Z.; Ibrahim, M.N. Design and Fabrication of Ultrasonic Tomographic Instrumentation System for Inspecting Flaw on Pipeline. Procedia Manuf. 2015, 2, 313–318.
    126. Rakvin, M.; Markučic, D.; Hižman, B. Evaluation of pipe wall thickness based on contrast measurement using Computed Radiography (CR). Procedia Eng. 2014, 69, 1216–1224.
    127. Suyama, F.M.; Delgado, M.R.; Dutra da Silva, R.; Centeno, T.M. Deep neural networks based approach for welded joint detection of oil pipelines in radiographic images with Double Wall Double Image exposure. NDT&E Int. 2019, 105, 46–55.
    128. Shafeek, H.I.; Gadelmawla, E.S.; Abdel-Shafy, A.A.; Elewa, I.M. Assessment of welding defects for gas pipeline radiographs using computer vision. NDT&E Int. 2004, 37, 291–299.
    129. Boaretto, N.; Centeno, T.M. Automated detection of welding defects in pipelines from radiographic images DWDI. NDT&E Int. 2017, 86, 7–13.
    130. Sun, J.G. Analysis of pulsed thermography methods for detect depth prediction. J. Heat Transf. 2006, 128, 329–338.
    131. Wu, D.; Busse, G. Lock-in thermography for nondestructive evaluation of materials. Rev. Gen. De Therm. 1998, 37, 693–703.
    132. Badghaish, A.A.; Fleming, D.C. Non-destructive inspection of composites using step heating thermography. J. Compos. Mater. 2008, 42, 1337–1357.
    133. Safizadeh, M.S.; Azizzadeh, T. NDT & E International Corrosion detection of internal pipeline using NDT optical inspection system. NDT&E Int. 2012, 52, 144–148.
    134. Abulkhanov, S.R.; Ivliev, N.A. Optical inspection device for the inner surface of pipe ends. J. Phys. Conf. Ser. 2019, 1368.
    135. Duran, O.; Althoefer, K.; Seneviratne, L.D. Automated sewer pipe inspection through image processing. IEEE Int. Conf. Robot. Autom. 2002, 3, 2551–2556.
    136. Inari, T.; Takashima, K.; Watanabe, M.; Fujimoto, J. Optical inspection system for the inner surface of a pipe using detection of circular images projected by a laser source. Measurement 1994, 13, 99–106.
    137. Robins, L. On-line Diagnostics Techniques in the Oil, Gas, and Chemical Industry. In Proceedings of the Third Middle East Non-destructive Testing Conference, Bahrain, Manama, 27–30 November 2005; pp. 27–30.
    138. Geiger, G. State-of-the-Art in Leak Detection and Localisation. In Proceedings of the Pipeline Technology Conference, Hannover, Germany, 25 April 2006.
    139. Brown, C.E.; Fingas, M.F. Review of the development of laser fluorosensors for oil spill application. Mar. Pollut. Bull. 2003, 47, 477–484.
    140. Liu, J.; Fu, M.; Wu, Z.; Su, H. An ELM-based classifier about MFL inspection of pipeline. In Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016, Yinchuan, China, 28–30 May 2016; pp. 1952–1955.
    141. Ninomiya, H. Raman lidar system for hydrogen gas detection. Opt. Eng. 2007, 46, 094301.
    142. Park, G.; Sohn, H.; Farrar, C.R.; Inman, D.J. Overview of piezoelectric impedance-based health monitoring and path forward. Shock Vib. Dig. 2003, 35, 451–463.
    143. Rosiek, M.; Martowicz, A.; Uhl, T. An overview of electromechanical impedance method for damage detection in mechanical structures. Eur. Workshop Struct. Health Monit. 2012, 2, 1376–1383.
    144. Zuo, C.; Feng, X.; Zhang, Y.; Lu, L.; Zhou, J. Crack detection in pipelines using multiple electromechanical impedance sensors. Smart Mater. Struct. 2017, 26.
    145. Na, W.S.; Lee, H. Experimental investigation for an isolation technique on conducting the electromechanical impedance method in high-temperature pipeline facilities. J. Sound Vib. 2016, 383, 210–220.
    146. Naidu, A.S.K.; Soh, C.K. Damage severity and propagation characterization with admittance signatures of piezo transducers. Smart Mater. Struct. 2004, 13, 393–403.
    147. Rosiek, A.M.; Martowicz, T.; Uhl, T.; Stepinski, T.L. Electromechanical Impedance for Damage Detection in Mechanical Structures. In Proceedings of the 11th IMEKO TC 10 Workshop on Smart Diagnostics of Structures, Krakow, Poland, 18–20 October 2010; pp. 1–8.
    148. Park, G.; Inman, D.J. Structural health monitoring using piezoelectric impedance measurements. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2007, 365, 373–392.
    149. Choe, H.B.; Lee, H.S.; Ismail, M.A.; Hussin, M.W. Evaluation of electrochemical impedance properties of anti-corrosion films by Arc thermal metal spraying method. Int. J. Electrochem. Sci. 2015, 10, 9775–9789.
    150. Eliyan, F.F.; Mahdi, E.S.; Alfantazi, A. Electrochemical evaluation of the corrosion behaviour of API-X100 pipeline steel in aerated bicarbonate solutions. Corros. Sci. 2012, 58, 181–191.
    151. Dann, M.R.; Maes, M.A. Stochastic corrosion growth modeling for pipelines using mass inspection data. Reliab. Eng. Syst. Saf. 2018, 180, 245–254.
    152. Dann, M.R.; Dann, C. Automated matching of pipeline corrosion features from in-line inspection data. Reliab. Eng. Syst. Saf. 2017, 162, 40–50.
    153. Caleyo, F.; Velázquez, J.C.; Valor, A.; Hallen, J.M. Probability distribution of pitting corrosion depth and rate in underground pipelines: A Monte Carlo study. Corros. Sci. 2009, 51, 1925–1934.
    154. Arzaghi, E.; Abbassi, R.; Garaniya, V.; Binns, J.; Chin, C.; Khakzad, N.; Reniers, G. Developing a dynamic model for pitting and corrosion-fatigue damage of subsea pipelines. Ocean Eng. 2018, 150, 391–396.
    155. Wu, K.Y.; Mosleh, A. Effect of temporal variability of operating parameters in corrosion modelling for natural gas pipelines subject to uniform corrosion. J. Nat. Gas Sci. Eng. 2019, 69, 102930.
    156. Sadeghioon, A.M.; Metje, N.; Chapman, D.N.; Anthony, C.J. SmartPipes: Smart wireless sensor networks for leak detection in water pipelines. J. Sens. Actuator Netw. 2014, 3, 64–78.
    157. Karray, F.; Garcia-ortiz, A.; Jmal, M.W.; Obeid, A.M. EARNPIPE: A Testbed for Smart Water Pipeline Monitoring using Wireless Sensor Network. Procedia Comput. Sci. 2016, 96, 285–294.
    158. Lai, T.T.T.; Chen, W.J.; Li, K.H.; Huang, P.; Chu, H.H. TriopusNet: Automating wireless sensor network deployment and replacement in pipeline monitoring. In Proceedings of the 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN), Beijing, China, 16–19 April 2012; pp. 61–71.
    159. Rashid, S.; Akram, U.; Khan, S.A. WML: Wireless Sensor Network based Machine Learning for Leakage Detection and Size Estimation. Procedia Comput. Sci. 2015, 63, 171–176.
    160. Ayadi, A.; Ghorbel, O.; Bensaleh, M.S.; Obeid, A.; Abid, M. Data classification in water pipeline based on wireless sensors networks. In Proceedings of the IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, Aqaba, Jordan, 28 October–1 November 2018; pp. 1212–1217.