Data-driven methods for multiple sensor streams, with applications in the maritime industry
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- Matematisk institutt [3782]
Abstract
No abstract.List of papers
Paper I: Brandsæter, A. and Vanem, E. (2018). Ship speed prediction based on full scale sensor measurements of shaft thrust and environmental conditions. Ocean Engineering, 162:316 – 330. DOI: 10.1016/j.oceaneng.2018.05.029. The article is included in the thesis. Also available at: https://doi.org/10.1016/j.oceaneng.2018.05.029 |
Paper II: Brandsæter, A., Vanem, E., and Glad, I. K. (2019). Efficient on-line anomaly detection for ship systems in operation. Expert Systems with Applications, 121:418–437. The article is included in the thesis. Also available at: https://doi.org/10.1016/j.eswa.2018.12.040 |
Paper III: Vanem, E. and Brandsæter, A. (2019). Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine. Journal of Marine Engineering & Technology, 1 – 18. DOI: 10.1080/20464177.2019.1633223. The article is included in the thesis. Also available at: https://doi.org/10.1080/20464177.2019.1633223 |
Paper IV: Brandsæter, A. and Knutsen, K. (2018). Towards a framework for assurance of autonomous navigation systems in the maritime industry. In Safety and Reliability–Safe Societies in a Changing World: Proceedings of ESREL 2018, (pp. 449-457). CRC Press. The article is included in the thesis. |
Paper V: Brandsæter, A. and Glad, I. K. (2019). Explainable artificial intelligence: How subsets of the training data affect a prediction. Submitted for publication. To be published. The paper is not available in DUO awaiting publishing. |