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Articles

Deep machine learning for structural health monitoring on ship hulls using acoustic emission method

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Pages 440-448 | Received 13 Sep 2019, Accepted 25 Feb 2020, Published online: 06 Mar 2020

References

  • [ABoS] American Bureau of Shipping. 2002. Technical analysis related to the prestige casualty on 13 November.
  • [ABS] American Bureau of Shipping. 2007. Hull survey for new construction.
  • Ahmed N, Natarajan T, Rao KR. 1974. Discrete cosine transform. IEEE Trans Comput. 100(1):90–93. doi: 10.1109/T-C.1974.223784
  • Anastasopoulos A, Kourousis D, Botten S, Wang G. 2009. Acoustic emission monitoring for detecting structural defects in vessels and offshore structures. J Sh Offshore Struct. 4(4):363–372. doi: 10.1080/17445300903133099
  • Bengio Y, Lamblin P, Popovici D, Larochelle H. 2007. Greedy layer-wise training of deep networks. In: Advances in neural information processing systems. Vol. 19. Vancouver, Canada: Neural Information Processing Systems Foundation, Inc. ( NIPS ); p. 153–160.
  • [FMAIO] French Marine Accident Investigation Office. 1999. Report of the enquiry into the sinking of the Erika off the coasts of Brittany on 12 December 1999.
  • Georgoulas G, Kappatos V, Nikolakopoulos G. 2016. Acoustic emission localization on ship hull structures using a deep learning approach. 23rd International Conference of Vibroengineering on Modeling, Identification and Fault Detection in Oil and Gas Equipment and Infrastructures, Istanbul, Turkey.
  • Georgoulas G, Stylios C, Kappatos V, Dermatas E. 2009. Wavelet usage for feature extraction for crack localization. 17th Mediterranean Conference on Control and Automation (MED'09). p. 1540–1545, Thessaloniki, Greece.
  • Heaton J. 2013. Artificial intelligence for humans. Deep learning and neural networks. Create space independent publishing platform. Chesterfield, Missouri: Heaton Research.
  • Hinton GE. 2010. A practical guide to training restricted Boltzmann machines. Momentum. 9(1):599–619.
  • Hinton GE, Roweis ST. 2002. Stochastic neighbor embedding. In: Becker S, Thrun S, Obermayer K, editors. Advances in neural information processing systems. Vol. 15. Vancouver, Canada: MIT Press; p. 833–840.
  • IMO. 2012. International shipping facts and figures. Information resources on trade, safety, security, environment. The International Maritime Organization (IMO) ; [accessed 2012 March 6]. www.imo.org.
  • [IUMI] International Union of Marine Insurance. 2009. Hull casualty statistics.
  • Jomdecha C, Prateepasen A, Kaewtrakulpong P. 2007. Study on source location using an acoustic emission system for various corrosion types. NDT&E Int. 40:584–593. doi: 10.1016/j.ndteint.2007.05.003
  • Kappatos V, Dermatas E. 2009. Neural localization of acoustic emission sources in ship hulls. J Mar Sci Technol. 14:248–255. doi: 10.1007/s00773-009-0051-8
  • Kappatos V, Dermatas E. 2011. Acoustic emission testing for the monitoring and detection of damage to ship hull structures. Theory and uses of acoustic emissions. Istanbul: Nova Publishers.
  • Kappatos V, Georgoulas G, Stylios C, Dermatas E. 2009. Evolutionary dimensionality reduction for crack localization in ship structures using a hybrid computational intelligent approach. International Joint Conference on Neural Networks. Atlanta, USA, p. 1531–1538.
  • Karvelis P, Georgoulas G, Tsoumas IP, Antonino-Daviu JA, Climente-Alarcón V, Stylios C. 2015. A symbolic representation approach for the diagnosis of broken rotor bars in induction motors. IEEE Trans Ind Inform. 11(5):1028–1037. doi: 10.1109/TII.2015.2463680
  • Keogh E, Chakrabarti K, Pazzani M, Mehrotra S. 2000. Dimensionality reduction for fast similarity search in large time series databases. J Knowled Inform Syst. 3(3):263–286. doi: 10.1007/PL00011669
  • LA. 2020. Looking ahead: strong growth for Asia Pacific ports. http://www.porttechnology.org/technical_papers/looking_ahead_strong_growth_for_asia_pacific_ports/.
  • [LMIU] Lloyd’s Marine Intelligence Unit. 2020. http://www.lloydslistintelligence.com/.
  • Løvaas S. 1985. Acoustic emission of offshore structures, attenuation – noise – crack monitoring. J Acoust Emiss. 4:161–164.
  • Maaten LVD, Hinton G. 2008. Visualizing data using t-SNE. J Mach Learn Res. 9:2579–2605.
  • Miller KR, Hill EVK, Patrick O, Moore OP. 2015. Nondestructive testing handbook, third edition. Acoustic emission testing, Columbus.
  • Nielsen A. 1980. Acoustic emission source based on pencil lead breaking . The Danish welding Institute Publication 80. Copenhagen.
  • Parry D. 1977. Nondestructive examination of subsea structures using acoustic emission technology. Offshore Technology Conference, Houston (TX).
  • Proakis JG, Manolakis DG. 1996. Digital signal processing: principles, algorithms, and applications. New Jersey: Prentice Hall.
  • Rao KR, Yip P. 2014. Discrete cosine transform: algorithms. Advantages, applications. Boston: Academic Press.
  • Rettig TW, Felsen MJ. 1976. Acoustic emission method for monitoring corrosion reactions. Corrosion. 32(4):121–126. doi: 10.5006/0010-9312-32.4.121
  • Roberts TM, Talebzadeh M. 2003. Acoustic emission monitoring of fatigue crack propagation. J Constr Steel Res. 59:695–712. doi: 10.1016/S0143-974X(02)00064-0
  • Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning internal representations by error propagation. In: Rumelhart DE, McClelland, JL, editors. Parallel distributed processing. Vol 1: foundations. Cambridge: MIT Press; p. 318-362.
  • SAX. 2016. http://www.cs.ucr.edu/~eamonn/SAX.htm.
  • Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Netw. 61:85–117. doi: 10.1016/j.neunet.2014.09.003
  • Seah KHW, Lim KB, Chew CH, Teoh SH. 1993. The correlation of acoustic emission with rate of corrosion. Corros Sci. 34(10):1707–1713. doi: 10.1016/0010-938X(93)90042-F
  • Strang G. 1999. The discrete cosine transform. SIAM Rev. 41(1):135–147. doi: 10.1137/S0036144598336745
  • Strathaus R, Bea R. 1992. Fatigue database development and analysis . Technical Report SMP 1-1. Department of Naval Architecture and Offshore Engineering, University of California at Berkeley.
  • Sucharski D. 1995. Crude oil tanker hull structure fracturing: an operator’s perspective. Proceedings of the Prevention of Fracture in Ship Structure, Washington, DC. p. 87–124.
  • Tamilselvan P, Wang P. 2013. Failure diagnosis using deep belief learning based health state classification. Reliab Eng Syst Saf. 15:124–135. doi: 10.1016/j.ress.2013.02.022
  • Thaulow C, Berge T. 1984. Acoustic emission monitoring of corrosion fatigue crack growth in offshore steel. NDT Int. 17(3):147–153. doi: 10.1016/0308-9126(84)90003-8
  • TMFSC. 2020. Thickness measurement for ship compliance. https://www.lr.org/en/thickness-measurement-for-ship-compliance/.
  • Tran VT, Althobiani F, Ball A. 2014. An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks. Expert Syst Appl. 41(9):4113–4122. doi: 10.1016/j.eswa.2013.12.026
  • Wasikowski M, Chen X. 2010. Combating the small sample class imbalance problem using feature selection. Knowledge and data engineering. IEEE Trans Knowl Data Eng. 22(10):1388–1400. doi: 10.1109/TKDE.2009.187
  • Witten IH, Frank E. 2005. Data mining: practical machine learning tools and techniques. San Francisco: Morgan Kaufmann.
  • Yi K, Faloutsos C. 2000. Fast time sequence indexing for arbitrary Lp norms. Proceedings of the 26th International Conference on Very Large Databases, Cairo.
  • Zhu Q, Zhang R. 2019. A classification supervised auto-encoder based on predefined evenly-distributed class centroids. ArXiv, abs/1902.00220.

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