Abstract
This paper aims to present an integrated methodology for designing of the ship monitoring system using machine learning algorithms. We collected ship monitoring data such as engine room monitoring system and AIS, and integrate and fuse data to form integrated ship information platform, the proposed methodology will train models using complete voyage data and then classify new data points using the improved Gaussian Mixture Model to get the most frequent operating regions of the main engine. Finally, we propose a scheme for performance evaluation of equipment using principal component analysis (PCA). This work will provide a flexible but robust framework for the early detection of emerging machinery faults. And provides a new way of thinking for the design of engine room monitoring system.
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References
Abramowski, T.: Application of artificial intelligence methods to preliminary design of ships and ship performance optimization. Naval Eng. J. 125(3), 101–112 (2013)
Huang, D., Zhao, D., Wei, L., et al.: Modeling and analysis in marine big data: advances and challenges. Math. Probl. Eng. (2) (2015)
Sang, L.Z., Wall, A., Mao, Z., et al.: A novel method for restoring the trajectory of the inland waterway ship by using AIS data. Ocean Eng. 110, 183–194 (2015)
Perera, L.P., Mo, B.: Data analytics for capturing marine engine operating regions for ship performance monitoring. In: The International Conference on Ocean, Offshore and Arctic Engineering (2016)
Gkerekos, C., Lazakis, I., Theotokatos, G.: Ship machinery condition monitoring using vibration data through supervised learning. In: The International Conference of Maritime Safety and Operations (2017)
Pallotta, G., Vespe, M., Bryan, K.: Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy 15(6), 2218–2245 (2013)
Wang, W.B., Zhong, R.T.: A clustering algorithm based on Greedy EM algorithm learning GMM. Comput. Simul. 24(2), 65–68 (2007)
Perera, L.P., Mo, B.: Marine engine operating regions under principal component analysis to evaluate ship performance and navigation behavior. In: IFAC Conference on Control Applications in Marine Systems (2016)
Gao, X., Yan, Z.: Comprehensive assessment of smart grid construction based on principal component analysis and cluster analysis. Power Syst. Technol. 37, 2238–2243 (2013)
Sun, F., Huang, L.Z., Liu, Y.F., et al.: A method of evaluating diesel engine performance by using data mining technology. J. Dalian Marit. Univ. 43(3), 83–88 (2017)
Acknowledgements
The study was supported by “the Fundamental Research Funds for the Central Universities”, (No. 3132016316). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Guanglei, L., Hong, Z., Dingyu, J., Hao, W. (2019). Design of Ship Monitoring System Based on Unsupervised Learning. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent, Interactive Systems and Applications. IISA 2018. Advances in Intelligent Systems and Computing, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-030-02804-6_36
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DOI: https://doi.org/10.1007/978-3-030-02804-6_36
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