Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
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A Case Study on the Establishment of Upper Control Limit to Detect Vessel's Main Engine Failures using Multivariate Control Chart

다변량 관리도를 활용한 선박 메인 엔진의 이상 관리 상한선 결정에 관한 연구

  • Received : 2017.10.19
  • Accepted : 2018.09.09
  • Published : 2018.12.20
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Abstract

Main engine failures in ship operations can lead to a major damage in terms of the vessel itself and the financial cost. In this respect, monitoring of a vessel's main engine condition is crucial in ensuring the vessel's performance and reducing the maintenance cost. The collection of a huge amount of vessel operational data in the maritime industry has never been easier with the advent of advanced data collection technologies. Real-time monitoring of the condition of a vessel's main engine has a potential to create significant value in maritime industry. This study presents a case study on the establishment of upper control limit to detect vessel's main engine failures using multivariate control chart. The case study uses sample data of an ocean-going vessel operated by a major marine services company in Korea, collected in the period of 2016.05-2016.07. This study first reviews various main engine-related variables that are considered to affect the condition of the main engine, and then attempts to detect abnormalities and their patterns via multivariate control charts. This study is expected to help to enhance the vessel's availability and provide a basis for a condition-based maintenance that can support proactive management of vessel's main engine in the future.

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References

  1. Boullosa, D., Larrabe, J. L., Lopez, A., & Gomez, M. A., 2017. Monitoring through $T^2$ hotelling of cylinder lubrication process of marine diesel engine. Applied Thermal Engineering, 110, pp.32-38. https://doi.org/10.1016/j.applthermaleng.2016.08.062
  2. Chen, Q., Kruger, U., Meronk, M. & Leung, A. Y. T., 2004. Synthesis of $T^2$ and Qstatistics for process monitoring, Control Engineering Practice, 12(6), pp.745-755. https://doi.org/10.1016/j.conengprac.2003.08.004
  3. Chongfuangprinya, P., Kim, S. B., Park, S. K. & Sukchotrat, T., 2011. Integration of support vector machines and control charts for multivariate process monitoring, Journal of Statistical Computation and Simulation, 81(9), pp. 1157-1173. https://doi.org/10.1080/00949651003789074
  4. Chou, Y. M., Mason, R. L., & Young, J. C., 2001. The control chart for individual observations from a multivariate non-normal distribution. Communications in statistics - Theory and methods, 30(8-9), pp.1937-1949. https://doi.org/10.1081/STA-100105706
  5. Efron, B. & Tibshirani, R., 1993, An introduction to the Bootstrap, Chapman & Hall/CRC, Roca Raton, USA.
  6. Gkerekos, C., Lazakis, I., & Theotokatos, G., 2017. Ship machinery condition monitoring using performance data through supervised learning, Proceedings of the Smart Ship Technology, London, UK, 24-25 January 2017, The Royal Institute of Naval Architects, pp.105-111.
  7. Jang, D. H., Moon, H. G., So, S.O., Seo, S. W., Han, S. H., Eun, C. G., & Kwon, J. G., 2011. Development of maintenance system for vessels and plants, Bulletin of the Society of Naval Architects of Korea, 39(2), pp.234-243.
  8. Lamaris, V. T., & Hountalas, D. T., 2010. A general purpose diagnostic technique for marine diesel engines-Application on the main propulsion and auxiliary diesel units of a marine vessel. Energy Conversion and Management, 51(4), pp.740-753. https://doi.org/10.1016/j.enconman.2009.10.031
  9. Lazakis, I., Dikis, K., Michala, A. L., & Theotokatos, G., 2016. Advanced ship systems condition monitoring for enhanced inspection, maintenance and decision making in ship operations. Transportation Research Procedia, 14, pp.1679-1688. https://doi.org/10.1016/j.trpro.2016.05.133
  10. Lee. H., Lee, J.S., & Lee, J. M., 2012. Marine Engine State Monitoring System using DPQ in CAN Network, Journal of Institute of Control, pp.13-20.
  11. Lowry, C. A., & Montgomery, D. C., 1995. A review of multivariate control charts. IIE Transactions, 27(6), pp.800-810. https://doi.org/10.1080/07408179508936797
  12. Mokashi, A. J., Wang, J., & Vermar, A. K., 2002. A study of reliability-centred maintenance in maritime operations. Marine Policy, 26(5), pp.325-335. https://doi.org/10.1016/S0308-597X(02)00014-3
  13. Orosa, J. A. et al., 2011. In Applications and Experiences of Quality Control. Online Ed. InTech.
  14. Phaladiganon, P., Kim, S. B., Chen, V. C. P., Baek, J. G., & Park, S. K., 2011. Bootstrap-based $T^2$ multivariate control charts, Communications in Statistics-Simulation and Computation, 40(5), pp.645-662. https://doi.org/10.1080/03610918.2010.549989
  15. Pillay, A., Wang, J., Wall, A. D., & Ruxton, T., 2001. A maintenance study of fishing vessel equipment using delay-time analysis. Journal of Quality in Maintenance Engineering, 7(2), pp.118-128. https://doi.org/10.1108/13552510110397421
  16. Raptodimos, Y., & Lazakis, I. (2018). Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications. Ships and Offshore Structures, pp.1-8.
  17. Runger, G. C., Alt, F. B., & Montgomery, D. C., 1996. Contributors to a multivariate statistical process control chart signal, Communications in Statistics-Theory and Methods-Policy & Management, 25(10), pp.2203-2213. https://doi.org/10.1080/03610929608831832
  18. Shewhart, W. A. (1926). Quality control charts. Bell Labs Technical Journal, 5(4), pp.593-603. https://doi.org/10.1002/j.1538-7305.1926.tb00125.x