Skip to main content

State-of-the-Art and Future Directions for Predictive Modelling of Offshore Structure Dynamics Using Machine Learning

Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Ramboll Oil and Gas are leading the field in the development of Structural Health Monitoring Systems (SHMS) for offshore structures. This paper outlines the State-of-the-Art process for predictive maintenance that Ramboll have developed and implemented for offshore structures. This system is one of the first, if not the only one, that creates a maintenance schedule based on knowledge of the structure’s current state.

The State-of-the-Art methods of today, as adopted by Ramboll, encompass advanced analysis methods ranging from linear and non-linear system identification, expansion processes, Bayesian FEM updating, wave load calibration, quantification of uncertainties from measured data, damage detection and structural re-assessment analysis to Risk- and Reliability-Based Inspection Planning (RBI) analysis.

The paper will be the first in a series of papers that will outline various promising methods contributing to an even better understanding of the issues at stake in the offshore structures context.

Keywords

  • Linear and nonlinear system identification
  • FEM updating
  • Modal expansion
  • Wave load calibration
  • Digital twin
  • Uncertainties
  • Predictive maintenance
  • Machine learning
  • Grey-box

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-74421-6_30
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-74421-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   299.99
Price excludes VAT (USA)
Hardcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 30.1
Fig. 30.2
Fig. 30.3
Fig. 30.4
Fig. 30.5
Fig. 30.6
Fig. 30.7
Fig. 30.8

References

  1. OGP Standard Committee: Reliability of offshore structures – current design and potential inconsistencies, OGP report no. 486. International Association of Oil and Gas Producers (OGP, IOGP) (Mar 2014)

    Google Scholar 

  2. Ramboll Oil and Gas: ROSAP, program ROSA, structural analysis, user’s guide. Ramboll Offshore Structural Analysis Program Package (ROSAP), Rev. 5.1 (Feb 2017)

    Google Scholar 

  3. Hansen, J.B., Brincker, R., Knudsen, M.B., Tygesen, U.: Combining GPS and integrated sensor signals. In: International Operational Modal Analysis Conference, Istanbul, Turkey (2011)

    Google Scholar 

  4. Skafte, A., Tygesen, U., Brincker, R.: Expansion of mode shapes and responses on the offshore platform Valdemar. In: International Modal Analysis Conference (IMAC), Orlando, FL, USA (2014)

    CrossRef  Google Scholar 

  5. Dascotte, E., Strobbe, J., Tygesen, U.T.: Continuous stress monitoring of large structures. In: International Operational Modal Analysis Conference (IOMAC), Guimaraes, Portugal (2013)

    Google Scholar 

  6. Brincker, R., Andersen, P.: Understanding stochastic subspace identification. In: Proceedings of the 24th International Modal Analysis Conference (IMAC), St. Louis, MO, USA (2006)

    Google Scholar 

  7. Peeters, B., Van der Auweraer, H., Guillaume, P., Leuridan, J.: The PolyMAX frequency-domain method: a new standard for modal parameter estimation Shock Vib. 11, 395–409 (2004.) IOS Press

    Google Scholar 

  8. Brincker, R., Zhang, L., Andersen, P.: Modal identification from ambient response using frequency domain decomposition. In: Proceedings of the 18th International Modal Analysis Conference (IMAC), San Antonio, TX, USA, pp. 625–630 (2000)

    Google Scholar 

  9. Zhang, L., Brincker, R., Andersen, P.: An overview of operational modal analysis: major development and issues. In: Proceedings of the 1st International Operational Modal Analysis Conference (IOMAC), Copenhagen, Denmark (2005)

    Google Scholar 

  10. Green, P.L., Tygesen, U.T., Stevanovic, N.: Bayesian modelling of offshore platforms. In: The Society for Experimental Mechanics (SEM), International Modal Analysis Conference (IMAC), Model Validation and Uncertainty Quantification, Orlando, FL, USA (2016)

    CrossRef  Google Scholar 

  11. Perisic, N., Kirkegaard, P.H., Tygesen, U.T.: Load identification of offshore platform for fatigue life estimation. In: International Modal Analysis Conference (IMAC), Orlando, FL, USA (2014)

    Google Scholar 

  12. Perisic, N., Tygesen, U.T.: Cost-effective load monitoring methods for fatigue life estimation of offshore platform. In: Proceedings from the ASME 2014 33rd International Conference on Ocean, Offshore and Artic Engineering (OMAE), San Francisco, CA, USA (2014)

    Google Scholar 

  13. Lauwagie, T., Guggenberger, J., Strobbe, J., Dascotte, E.: Model updating using operational data. In: International Conference on Noise and Vibration Engineering (ISMA), Leuven, Belgium (2010)

    Google Scholar 

  14. O’Callahan, J., Avitabile, P., Riemer, R.: System Equivalent Reduction Expansion Process (SEREP). In: Proceeding of the 7th International Modal Analysis Conference (IMAC), pp. 29–37 (1989)

    Google Scholar 

  15. Sohn, H., Law, K.H.: Extraction of Ritz vectors from vibration test data. Mech. Syst. Signal Process. 15, 231–226 (2001)

    CrossRef  Google Scholar 

  16. Skafte, A., Kristoffersen, J., Vestermark, J., Tygesen, U.T., Brincker, R.: Experimental study of strain prediction on wave induced structures using modal decomposition and quasi static Ritz vectors. J. Eng. Struct. 136, 261–276 (2017.) Elsevier

    CrossRef  Google Scholar 

  17. Fletcher, R.: Practical Methods of Optimization, 2nd edn. John Wiley & Sons (2013). https://doi.org/10.1002/9781118723203

  18. Simon, D.: Evolutionary Optimization Algorithms. John Wiley & Sons, Inc., Hoboken, New Jersey (2013)

    Google Scholar 

  19. Ulriksen, M.D., Tcherniak, D., Hansen, L.M., Johansen, R.J., Damkilde, L., Frøyd, L.: In-situ damage localization for a wind turbine blade through outlier analysis of SDDLV-induced stress resultants. Struct. Health Monit. 16, 745–761 (2017)

    CrossRef  Google Scholar 

  20. Ulriksen, M.D., Damkilde, L.: Structural Damage Localization by Outlier Analysis of Signal-processed Mode Shapes: Analytical and Experimental Validation. Mechanical Systems and Signal Processing. 68-69(February), 1–14 (2015). https://doi.org/10.1016/j.ymssp.2015.07.021

    CrossRef  Google Scholar 

  21. Dohler, M., Hille, F.: Subspace-based damage detection on steel frame structure under changing excitation. In: International Modal Analysis Conference (IMAC), Orlando, FL, USA (2014)

    CrossRef  Google Scholar 

  22. DNVGL-RP-C210: Probabilistic Methods for Planning of Inspection Planning for Fatigue Cracks in Offshore Structures. DNV-GL Recommended Practice, Edition (Nov 2015)

    Google Scholar 

  23. Rogers, T., Holmes, G.R., Cross, E.J., Worden, K.: On a Grey Box modelling framework for nonlinear system identification. In: Special Topics in Structural Dynamics, vol. 6, pp. 167–178. Springer Link (Mar 2017)

    CrossRef  Google Scholar 

  24. Worden, K., Rogers, T., Cross, E.J.: Identification of nonlinear wave forces using Gaussian process NARX models. In: Nonlinear Dynamics, vol. 1, pp. 203–221. Springer Link (May 2017)

    CrossRef  Google Scholar 

  25. Dervilis, N., Cross, E.J., Barthorpe, R.J., Worden, K.: Robust methods of inclusive outlier analysis for structural health monitoring. J. Sound Vib. 333, 5181–5195 (2014)

    CrossRef  Google Scholar 

  26. Cross, E.J., Worden, K., Chen, Q.: Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data. Proc. R. Soc. A. 467, 2712–2732 (2011). https://doi.org/10.1098/rspa.2011.0023

    CrossRef  MATH  Google Scholar 

Download references

Acknowledgement

The authors wish to thank the oil and gas operators in the Danish North Sea: Maersk Oil, Hess Denmark and DONG Energy for their participation in several projects forming the basis for the developed methods as of today. TR specially wishes to thank Ramboll for providing financial support for this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to U. T. Tygesen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 The Society for Experimental Mechanics, Inc.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Tygesen, U.T., Worden, K., Rogers, T., Manson, G., Cross, E.J. (2019). State-of-the-Art and Future Directions for Predictive Modelling of Offshore Structure Dynamics Using Machine Learning. In: Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-74421-6_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74421-6_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74420-9

  • Online ISBN: 978-3-319-74421-6

  • eBook Packages: EngineeringEngineering (R0)