{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T04:08:14Z","timestamp":1783397294141,"version":"3.54.6"},"reference-count":54,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["P0015306"],"award-info":[{"award-number":["P0015306"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not explain why the model classifies specific data instances as an anomaly. This study combines explainable AI techniques with anomaly detection algorithm to overcome the limitation above. As an explainable AI method, this study adopts Shapley Additive exPlanations (SHAP), which is theoretically solid and compatible with any kind of machine learning algorithm. SHAP enables us to measure the marginal contribution of each sensor variable to an anomaly. Thus, one can easily specify which sensor is responsible for the specific anomaly. To illustrate our framework, the actual sensor stream obtained from the cargo vessel collected over 10 months was analyzed. In this analysis, we performed hierarchical clustering analysis with transformed SHAP values to interpret and group common anomaly patterns. We showed that anomaly interpretation and segmentation using SHAP value provides more useful interpretation compared to the case without using SHAP value.<\/jats:p>","DOI":"10.3390\/s21155200","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T21:44:32Z","timestamp":1627854272000},"page":"5200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data"],"prefix":"10.3390","volume":"21","author":[{"given":"Donghyun","family":"Kim","sequence":"first","affiliation":[{"name":"Korea Marine Equipment Research Institute, Busan 49111, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gian","family":"Antariksa","sequence":"additional","affiliation":[{"name":"Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9950-2382","authenticated-orcid":false,"given":"Melia Putri","family":"Handayani","sequence":"additional","affiliation":[{"name":"Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangbong","family":"Lee","sequence":"additional","affiliation":[{"name":"Lab021 Shipping Analytics, Busan 48508, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9380-5470","authenticated-orcid":false,"given":"Jihwan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.oceaneng.2016.10.029","article-title":"Data analysis on marine engine operating regions in relation to ship navigation","volume":"128","author":"Perera","year":"2016","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.17818\/NM\/2018\/1.8","article-title":"Veliki skupovi podataka u pomorskoj industriji","volume":"65","author":"Milicevic","year":"2018","journal-title":"Na\u0161e More"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106282","DOI":"10.1016\/j.oceaneng.2019.106282","article-title":"Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study","volume":"188","author":"Gkerekos","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mak, L., Sullivan, M., Kuczora, A., and Millan, J. 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