Research Papers: Pipeline Systems

An Integrated Prognostics Approach for Pipeline Fatigue Crack Growth Prediction Utilizing Inline Inspection Data

[+] Author and Article Information
Mingjiang Xie, Zhigang Tian

Department of Mechanical Engineering,
University of Alberta,
Edmonton, AB T6G 2R3, Canada

Steven Bott, Aaron Sutton, Alex Nemeth

Enbridge Liquid Pipelines,
Edmonton, AB T5J 0T6, Canada

1Corresponding author.

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received November 11, 2017; final manuscript received March 16, 2018; published online April 20, 2018. Assoc. Editor: Oreste S. Bursi.

J. Pressure Vessel Technol 140(3), 031702 (Apr 20, 2018) (10 pages) Paper No: PVT-17-1228; doi: 10.1115/1.4039780 History: Received November 11, 2017; Revised March 16, 2018

Fatigue cracking is a key type of defect for liquid pipelines, and managing fatigue cracks has been a top priority and a big challenge for liquid pipeline operators. The existing inline inspection (ILI) tools for pipeline defect evaluation have large fatigue crack measurement uncertainties. Furthermore, the current physics-based methods are mainly used for fatigue crack growth prediction, where the same or a small range of fixed model parameters is used for all pipes. They result in uncertainty that is managed through the use of conservative safety factors such as adding depth uncertainty to the measured depth in deciding integrity management and risk mitigation strategies. In this study, an integrated approach is proposed for pipeline fatigue crack growth prediction utilizing ILI data including consideration of crack depth measurement uncertainty. This approach is done by integrating the physical models, including the stress analysis models, the crack growth model governed by the Paris’ law, and the ILI data. With the proposed integrated approach, the finite element (FE) model of a cracked pipe is built and the stress analysis is performed. ILI data are utilized to update the uncertain physical parameters for the individual pipe being considered so that a more accurate fatigue crack growth prediction can be achieved. Time-varying loading conditions are considered in the proposed integrated method by using rainflow counting method. The proposed integrated prognostics approach is compared with the existing physics-based method using examples based on simulated data. Field data provided by a Canadian pipeline operator are also employed for the validation of the proposed method. The examples and case studies in this paper demonstrate the limitations of the existing physics-based method, and the promise of the proposed method for achieving accurate fatigue crack growth prediction as continuous improvement of ILI technologies further reduces ILI measurement uncertainty.

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CEPA, 2012, “ About Pipelines, Our Energy Connections,” Canadian Energy Pipeline Association, Calgary, AB, Canada, Report.
Mohitpour, M. , Murray, A. , McManus, M. , and Colquhoun, I. , 2010, Pipeline Integrity Assurance: A Practical Approach, American Society of Mechanical Engineers, New York. [CrossRef]
Zarea, M. , Piazza, M. , Vignal, G. , Jones, C. , Rau, J. , and Wang, R. , 2013, “ Review of R&D in Support of Mechanical Damage Threat Management in Onshore Transmission Pipeline Operations,” ASME Paper No. IPC2012-90654.
PRCI, 2014, “ 2014-Year in Review,” Pipeline Research Council International, Chantilly, VA, Report.
Nielsen, A. , Mallet-Paret, J. , and Griffin, K. , 2014, “ Probabilistic Modeling of Crack Threats and the Effects of Mitigation,” ASME Paper No. IPC2014-33511.
Sutton, A. , Hubert, Y. , Textor, S. , and Haider, S. , 2014, “ Allowable Pressure Cycling Limits for Liquid Pipelines,” ASME Paper No. IPC2014-33566.
Jardine, A. K. S. , Lin, D. , and Banjevic, D. , 2006, “ A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance,” Mech. Syst. Signal Process., 20(7), pp. 1483–1510. [CrossRef]
Mansor, N. I. I. , Abdullah, S. , Ariffin, A. K. , and Syarif, J. , 2014, “ A Review of the Fatigue Failure Mechanism of Metallic Materials Under a Corroded Environment,” Eng. Failure Anal., 42, pp. 353–365. [CrossRef]
Zhao, F. , Tian, Z. , and Zeng, Y. , 2013, “ Uncertainty Quantification in Gear Remaining Useful Life Prediction Through an Integrated Prognostics Method,” IEEE Trans. Reliab., 62(1), pp. 146–159. [CrossRef]
Bott, S. , and Sporns, R. , 2008, “ The Benefits and Limitations of Using Risk Based Probabilistic and Deterministic Analysis for Monitoring and Mitigation Planning,” ASME Paper No. IPC2008-64539.
Hong, S. W. , Koo, J. M. , Seok, C. S. , Kim, J. W. , Kim, J. H. , and Hong, S. K. , 2015, “ Fatigue Life Prediction for an API 5 L X42 Natural Gas Pipeline,” Eng. Failure Anal., 56, pp. 396–402. [CrossRef]
Pinheiro , B. D, C. , and Pasqualino, I. P. , 2009, “ Fatigue Analysis of Damaged Steel Pipelines Under Cyclic Internal Pressure,” Int. J. Fatigue, 31(5), pp. 962–973. [CrossRef]
Oikonomidis, F. , Shterenlikht, A. , and Truman, C. E. , 2014, “ Prediction of Crack Propagation and Arrest in X100 Natural Gas Transmission Pipelines With a Strain Rate Dependent Damage Model (SRDD)—Part 2: Large Scale Pipe Models With Gas Depressurisation,” Int. J. Pressure Vessels Piping, 122, pp. 15–21. [CrossRef]
Oikonomidis, F. , Shterenlikht, A. , and Truman, C. E. , 2013, “ Prediction of Crack Propagation and Arrest in X100 Natural Gas Transmission Pipelines With the Strain Rate Dependent Damage Model (SRDD)—Part 1: A Novel Specimen for the Measurement of High Strain Rate Fracture Properties and Validation of the SRDD Model Parameters,” Int. J. Pressure Vessels Piping, 105–106, pp. 60–68. [CrossRef]
Varela, F. , Yongjun Tan, M. , and Forsyth, M. , 2015, “ An Overview of Major Methods for Inspecting and Monitoring External Corrosion of on-Shore Transportation Pipelines,” Corros. Eng., Sci. Technol., 50(3), pp. 226–235. [CrossRef]
Bates, N. , Lee, D. , and Maier, C. , 2010, “ A Review of Crack Detection In-Line Inspection Case Studies,” ASME Paper No. IPC2010-31114.
Slaughter, M. , Spencer, K. , Dawson, J. , and Senf, P. , 2010, “ Comparison of Multiple Crack Detection In-Line Inspection Data to Assess Crack Growth,” ASME Paper No. IPC2010-31255.
Wang, H. , Yajima, A. , Y. Liang, R. , and Castaneda, H. , 2015, “ A Bayesian Model Framework for Calibrating Ultrasonic In-Line Inspection Data and Estimating Actual External Corrosion Depth in Buried Pipeline Utilizing a Clustering Technique,” Struct. Saf., 54, pp. 19–31. [CrossRef]
Zhao, F. , Tian, Z. , Bechhoefer, E. , and Zeng, Y. , 2015, “ An Integrated Prognostics Method Under Time-Varying Operating Conditions,” IEEE Trans. Reliab., 64(2), pp. 673–686. [CrossRef]
Roshanfar, M. , and Salimi, M. H. , 2015, “ Comparing of Methods of Cycle Calculating and Counting to the Rain Flow Method,” Ext. J. App. Sci., 3(7), pp. 291–296.
NIST, Materials Reliability Division, 2007, “ Mechanical Properties and Crack Behavior in Line Pipe Steels,” National Institute of Standards and Technology Workshop, Boulder, CO, Report No. NISTIR 6649.
Al-Muslim, H. M. , and Arif, A. F. M. , 2010, “ Effect of Geometry, Material and Pressure Variability on Strain and Stress Fields in Dented Pipelines Under Static and Cyclic Pressure Loading Using Probability Analysis,” ASME Paper No. IPC2010-31246.
Silva, J. , Ghaednia, H. , and Das, S. , 2012, “ Fatigue Life Assessment for NPS30 Steel Pipe,” ASME Paper No. IPC2012-90081.
Shim, D. , and Wilkowski, G. , 2014, “ Bulging Factor for Axial Surface Cracks in Pipes,” ASME Paper No. IPC2014-33440.
Newman, J. , and Raju, I. , 1981, “ An Empirical Stress-Intensity Factor Equation for the Surface Crack,” Eng. Fract. Mech., 15(1–2), pp. 185–192. [CrossRef]
Baker, M., Jr. , 2004, “ Low Frequency ERW and Lap Welded Longitudinal Seam Evaluation,” Kiefner and Associates, Inc., Columbus, OH, Final Report.
Chookah, M. , Nuhi, M. , and Modarres, M. , 2011, “ A Probabilistic Physics-of-Failure Model for Prognostic Health Management of Structures Subject to Pitting and Corrosion-Fatigue,” Reliab. Eng. Syst. Saf., 96(12), pp. 1601–1610. [CrossRef]
Jin, Q. , Sun, Z. , and Guo, W. , 2014, “ Experimental and Finite Element Study on the Fatigue Growth of a Semi-Elliptical Surface Crack in aX80 Pipeline Steelspecimen,” Advances in Civil and Industrial Engineering IV, G. Li , C. Chen , B. Jiang , and Q. Shen , eds., Trans Tech Publications, Zurich, Switzerland, pp. 3026–3029.
Carpinteri, A. , and Brighenti, R. , 1998, “ Circumferential Surface Flaws in Pipes Under Cyclic Axial Loading,” Eng. Fract. Mech., 60(4), pp. 383–396. [CrossRef]
Carpinteri, A. , 1993, “ Shape Change of Surface Cracks in Round Bars Under Cyclic Axial Loading,” Int. J. Fatigue, 15(1), pp. 21–26. [CrossRef]
Zhao, F. , Tian, Z. , and Zeng, Y. , 2013, “ A Stochastic Collocation Approach for Efficient Integrated Gear Health Prognosis,” Mech. Syst. Signal Process., 39(1–2), pp. 372–387. [CrossRef]
Enbridge and NDT Global, 2017, “ Enbridge and NDT Global: A Multi-year Collaboration Agreement,” NDT Global, Calgary, AB, Canada, accessed Apr. 10, 2018 https://www.ndt-global.com/news/enbridge-partnership-to-advance-pipeline-technology-innovation


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Fig. 2

Crack built in ANSYS workbench

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Fig. 3

The fitted SIF functions

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Fig. 4

Comparison of SIF results between the Raju and Newman method and the FE method

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Fig. 5

Ten simulated degradation paths

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Fig. 6

Distributions of parameter m for path #6

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Fig. 7

Distributions of predicted failure time for path #6

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Fig. 8

Distributions of predicted failure time for path #7

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Fig. 9

Distributions of predicted failure time for path #8

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Fig. 10

Ten simulated degradation paths with different starting points

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Fig. 11

Distributions of parameter m for path #6

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Fig. 12

Distributions of predicted failure time for path #6

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Fig. 13

Total pressure data from February 6, 2003 to March 31, 2007

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Fig. 14

Rainflow-counting result

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Fig. 15

Degradation paths generated using matrix 1

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Fig. 16

Relationship between failure stress and flaw size

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Fig. 17

Real crack growth curve



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