Monitoring the Performance of a Ship’s Main Engine Based on Big Data Technology

Meng Liang; Mingzhi Chen

doi:10.2478/pomr-2022-0033

References

1. B. Qiao, W. He, Y. Tian, Y. Liu, O. Cai, and Y. Li, “Ship emission reduction effect evaluation of air pollution control countermeasures,” Transportation Research Procedia, vol. 25, pp. 3606-3618, 2017, doi: 10.1016/j.trpro.2017.05.325.
Open DOI Search in Google Scholar Back to article
2. F. Jasper, H. Shinichi et al., “Fourth IMO Greenhouse Gas Study 2020,” International Maritime Organisation (IMO), London, UK, 2020.
Search in Google Scholar Back to article
3. V. Kuznetsov, B. Dymo, S. Kuznetsova, M. Bondarenko, and A. Voloshyn, “Improvement of the cargo fleet vessels power plants ecological indexes by development of the exhaust gas systems,” Polish Maritime Research, vol. 28, pp. 97-104, 2021, doi: 10.2478/pomr-2021-0009.
Open DOI Search in Google Scholar Back to article
4. I. Ančić and A. Šestan, “Influence of the required EEDI reduction factor on the CO2 emission from bulk carriers,” Energy Policy, vol. 84, pp. 107-116, 2015, doi: 10.1016/j.enpol.2015.04.031.
Open DOI Search in Google Scholar Back to article
5. E.K. Hansen, H.B. Rasmussen, and M. Lützen, “Making shipping more carbon-friendly? Exploring ship energy efficiency management plans in legislation and practice,” Energy Research & Social Science, vol. 65, pp. 101459, 2020, doi: 10.1016/j.erss.2020.101459.
Open DOI Search in Google Scholar Back to article
6. M. Kalajdžić, M. Vasilev, and N. Momčilović, “Power Reduction Considerations for Bulk Carriers with respect to Novel Energy Efficiency Regulations,” Brodogradnja: Teorija i praksa brodogradnje i pomorske tehnike, vol. 73, pp. 79-92, 2022, doi: 10.21278/brod72205.
Open DOI Search in Google Scholar Back to article
7. L. Fedi, “The Monitoring, Reporting and Verification of Ships’ Carbon Dioxide Emissions: A European Substantial Policy Measure towards Accurate and Transparent Carbon Dioxide Quantification,” Ocean Yearbook Online, vol. 31, pp. 381-417, 2017, doi: 10.1163/22116001-03101015.
Open DOI Search in Google Scholar Back to article
8. W. Tarełko, “The effect of hull biofouling on parameters characterising ship propulsion system efficiency,” Polish Maritime Research, vol. 21, pp. 27-34, 2014, doi: 10.2478/pomr-2014-0038.
Open DOI Search in Google Scholar Back to article
9. P. Król, «Hydrodynamic state of art review: rotor–stator marine propulsor systems design,» Polish Maritime Research, vol. 28, pp. 72-82, 2021, doi: 10.2478/pomr-2021-0007.
Open DOI Search in Google Scholar Back to article
10. P. Puzdrowska, «Diagnostic information analysis of quickly changing temperature of exhaust gas from marine diesel engine. Part i single factor analysis,» Polish Maritime Research, vol. 28, pp. 97-106, 2021, doi: 10.2478/pomr-2021-0052.
Open DOI Search in Google Scholar Back to article
11. P. Król, “Blade section profile array lifting surface design method for marine screw propeller blade,” Polish Maritime Research, vol. 26, pp. 134-141, 2019, doi: 10.2478/pomr-2019-0075.
Open DOI Search in Google Scholar Back to article
12. K. Rudzki and W. Tarelko, “A decision-making system supporting selection of commanded outputs for a ship’s propulsion system with a controllable pitch propeller,” Ocean Engineering, vol. 126, pp. 254-264, 2016, doi: 10.1016/j.oceaneng.2016.09.018
Open DOI Search in Google Scholar Back to article
13. R. Varbanets, V. Zalozh, A. Shakhov, I. Savelieva, and V. Piterska, “Determination of top dead centre location based on the marine diesel engine indicator diagram analysis,” Diagnostyka, vol. 21, pp. 51-60, 2020, doi: 10.29354/diag/116585.
Open DOI Search in Google Scholar Back to article
14. S. Park, S. W. Park, S. H. Rhee, S. B. Lee, J. E. Choi, and S. H. Kang, “Investigation on the wall function implementation for the prediction of ship resistance,” International Journal of Naval Architecture and Ocean Engineering, vol. 5, pp. 33-46, 2013, doi: 10.2478/IJNAOE-2013-0116.
Open DOI Search in Google Scholar Back to article
15. M.B. Samsul, “Blade cup method for cavitation reduction in marine propellers,” Polish Maritime Research, 2021, doi: 10.2478/pomr-2021-0021, doi: 10.2478/pomr-2021-0021.
Open DOI Search in Google Scholar Back to article
16. M.H. Ghaemi, “Performance and emission modelling and simulation of marine diesel engines using publicly available engine data,” Polish Maritime Research, vol. 28, pp. 63-87, 2021, doi: 10.2478/pomr-2021-0050.
Open DOI Search in Google Scholar Back to article
17. B.D. Brouer, C.V. Karsten, and D. Pisinger, “Big data optimisation in maritime logistics,” Big data optimisation: Recent developments and challenges. Springer, Cham, vol. 18, pp. 319-344, 2016, doi: 10.1007/978-3-319-30265-2_14.
Open DOI Search in Google Scholar Back to article
18. X. Zeng and M. Chen, “A Novel Big Data Collection System for Ship Energy Efficiency Monitoring and Analysis Based on BeiDou System,” Journal of Advanced Transportation, vol. 2021, pp.1-10, 2021, doi: 10.1155/2021/9914720.
Open DOI Search in Google Scholar Back to article
19. I. Zaman, K. Pazouki, R. Norman, S. Younessi, and S. Coleman, “Challenges and opportunities of big data analytics for upcoming regulations and future transformation of the shipping industry,” Procedia engineering, vol. 194, pp. 537-544, 2017, doi: 10.1016/j.proeng.2017.08.182.
Open DOI Search in Google Scholar Back to article
20. A. Fan, X. Yan, and Q. Yin, “A multisource information system for monitoring and improving ship energy efficiency,” Journal of Coastal Research, vol.32, pp. 1235-1245, 2016, doi: 10.2112/JCOASTRES-D-15-00234.1.
Open DOI Search in Google Scholar Back to article
21. J. Deng, J. Zeng, S. Mai, B. Jin, B. Yuan, Y. You. S. Lu, and M.Yang, «Analysis and prediction of ship energy efficiency using 6G big data internet of things and artificial intelligence technology,» International Journal of System Assurance Engineering and Management, vol. 12, pp. 824–834, 2021, doi: 10.1007/s13198-021-01116-9.
Open DOI Search in Google Scholar Back to article
22. T. Niksa-Rynkiewicz, N. Szewczuk-Krypa, A. Witkowska, K. Cpałka, M. Zalasiński, and A. Cader, “Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network,” Journal of Artificial Intelligence and Soft Computing Research, vol. 11, pp. 143-155, 2021, doi: 10.2478/jaiscr-2021-0009.
Open DOI Search in Google Scholar Back to article
23. A. Witkowska and T. Niksa-Rynkiewicz, «Dynamically positioned ship steering making use of backstepping method and artificial neural networks,» Polish Maritime Research, vol. 25, pp. 5-12, 2018, doi: 10.2478/pomr-2018-0126.
Open DOI Search in Google Scholar Back to article
24. S. García, J. Luengo, and F. Herrera, “Data preprocessing in data mining,” Cham, Switzerland: Springer International Publishing, vol. 72, pp. 59-139, 2015, doi: 10.1007/978-3-319-10247-4.
Open DOI Search in Google Scholar Back to article
25. L.P. Perera and B. Mo, “Ship performance and navigation information under high-dimensional digital models,” Journal of Marine Science and Technology, vol. 25(1), pp. 59-139, 2020, doi: 10.1007/978-3-319-10247-4.
Open DOI Search in Google Scholar Back to article
26. Y. Raptodimos and I. Lazakis, “Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications,” Ships and Offshore Structures, vol. 13, pp. 649-656, 2018, doi: 10.1080/17445302.2018.1443694.
Open DOI Search in Google Scholar Back to article
27. E. Vanem and A. Brandsæter, “Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine,” Journal of Marine Engineering & Technology, vol. 20, pp. 217-234, 2021, doi: 10.1080/20464177.2019.1633223.
Open DOI Search in Google Scholar Back to article
28. L. P. Perera, and B. Mo, “Data analytics for capturing marine engine operating regions for ship performance monitoring,” International Conference on Offshore Mechanics and Arctic Engineering, American Society of Mechanical Engineers, 2016, Vol. 49989, doi: 10.1115/OMAE2016-54168
Open DOI Search in Google Scholar Back to article
29. L. P. Perera, and B. Mo, “Marine engine operating regions under principal component analysis to evaluate ship performance and navigation behaviour,” IFACPapersOnLine, vol. 49(23), pp. 512-517, 2016, doi: 10.1016/j.ifacol.2016.10.487.
Open DOI Search in Google Scholar Back to article
30. X. Yan, K. Wang, Y. Yuan, X. Jiang, and R. R. Negenborn, “Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors,” Ocean Engineering, vol. 169, pp. 457-468, 2018, doi: 10.1016/j.oceaneng.2018.08.050.
Open DOI Search in Google Scholar Back to article
31. K. Wang, X. Yan, Y. Yuan, X. Jiang, G. Lodewijks, and R. R. Negenborn, «Study on route division for ship energy efficiency optimisation based on big environment data,» 2017 4th International Conference on Transportation Information and Safety (ICTIS), IEEE, pp. 111-116, 2017, doi: 10.1109/ICTIS.2017.8047752.
Open DOI Search in Google Scholar Back to article
32. R. Adland, P. Cariou, H. Jia, and F. C. Wolff, “The energy efficiency effects of periodic ship hull cleaning,” Journal of Cleaner Production, vol. 178, pp. 1–13, 2018, doi: 10.1016/j.jclepro.2017.12.247.
Open DOI Search in Google Scholar Back to article
33. O. Loyola-Gonzalez, “Black box vs. white-box: Understanding their advantages and weaknesses from a practical point of view, “ IEEE Access, vol. 7, pp. 154096–154113, 2019, doi: 10.1109/ACCESS.2019.2949286.
Open DOI Search in Google Scholar Back to article
34. X. Zeng, M. Chen, H. Li and X. Wu, “A Data-Driven Intelligent Energy Efficiency Management System for Ships,” IEEE Intelligent Transportation Systems Magazine, doi: 10.1109/MITS.2022.3153491.
Open DOI Search in Google Scholar Back to article
35. M. Ester, H. P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” kdd, vol. 96, pp. 226-231, 1996.
Search in Google Scholar Back to article
36. R. Varbanets, V. Klymenko, O. Fomin, V. Píštěk, P. Kučera, D. Minchev, A. Khrulev, and V. Zalozh, “Acoustic method for estimation of marine low-speed engine turbocharger parameters,” Journal of Marine Science and Engineering, vol. 9, 2021, doi: 10.3390/jmse9030321.
Open DOI Search in Google Scholar Back to article
37. A. GéRon, “Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”. Sebastopol, CA, USA: O’Reilly Media, 2017.
Search in Google Scholar Back to article

Monitoring the Performance of a Ship’s Main Engine Based on Big Data Technology

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