dc.contributor.author | Ellefsen, Andre | |
dc.contributor.author | Cheng, Xu | |
dc.contributor.author | Holmeset, Finn Tore | |
dc.contributor.author | Ushakov, Sergey | |
dc.contributor.author | Æsøy, Vilmar | |
dc.contributor.author | Zhang, Houxiang | |
dc.date.accessioned | 2019-09-06T08:59:46Z | |
dc.date.available | 2019-09-06T08:59:46Z | |
dc.date.created | 2019-09-03T16:39:41Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-1-7281-1699-0 | |
dc.identifier.uri | http://hdl.handle.net/11250/2612887 | |
dc.description.abstract | The maritime industry generally anticipates having semi-autonomous ferries in commercial use on the west coast of Norway by the end of this decade. In order to schedule maintenance operations of critical components in a secure and cost-effective manner, a reliable prognostics and health management system is essential during autonomous operations. Any remaining useful life prediction obtained from such system should depend on an automatic fault detection algorithm. In this study, an unsupervised reconstruction-based fault detection algorithm is used to predict faults automatically in a simulated autonomous ferry crossing operation. The benefits of the algorithm are confirmed on data sets of real-operational data from a marine diesel engine collected from a hybrid power lab. During the ferry crossing operation, the engine is subjected to drastic changes in operational loads. This increases the difficulty of the algorithm to detect faults with high accuracy. Thus, to support the algorithm, three different feature selection processes on the input data is compared. The results suggest that the algorithm achieves the highest prediction accuracy when the input data is subjected to feature selection based on sensitivity analysis. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | IEEE conference proceedings | nb_NO |
dc.relation.ispartof | IEEE International Conference on Mechatronics and Automation | |
dc.relation.ispartofseries | IEEE International Conference on Mechatronics and Automation; | |
dc.title | Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 6 | nb_NO |
dc.source.issue | 2195-2200 | nb_NO |
dc.identifier.doi | 10.1109/ICMA.2019.8816600 | |
dc.identifier.cristin | 1721157 | |
dc.relation.project | Norges forskningsråd: 280703 | nb_NO |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,64,93,0 | |
cristin.unitcode | 194,64,20,0 | |
cristin.unitname | Institutt for havromsoperasjoner og byggteknikk | |
cristin.unitname | Institutt for marin teknikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |