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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Ramboll Oil and Gas: ROSAP, program ROSA, structural analysis, user’s guide. Ramboll Offshore Structural Analysis Program Package (ROSAP), Rev. 5.1 (Feb 2017)
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)
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)
Dascotte, E., Strobbe, J., Tygesen, U.T.: Continuous stress monitoring of large structures. In: International Operational Modal Analysis Conference (IOMAC), Guimaraes, Portugal (2013)
Brincker, R., Andersen, P.: Understanding stochastic subspace identification. In: Proceedings of the 24th International Modal Analysis Conference (IMAC), St. Louis, MO, USA (2006)
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
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)
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)
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)
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)
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)
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)
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)
Sohn, H., Law, K.H.: Extraction of Ritz vectors from vibration test data. Mech. Syst. Signal Process. 15, 231–226 (2001)
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
Fletcher, R.: Practical Methods of Optimization, 2nd edn. John Wiley & Sons (2013). https://doi.org/10.1002/9781118723203
Simon, D.: Evolutionary Optimization Algorithms. John Wiley & Sons, Inc., Hoboken, New Jersey (2013)
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)
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
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)
DNVGL-RP-C210: Probabilistic Methods for Planning of Inspection Planning for Fatigue Cracks in Offshore Structures. DNV-GL Recommended Practice, Edition (Nov 2015)
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)
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)
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)
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
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 The Society for Experimental Mechanics, Inc.
About this paper
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)