7 Papers
10 Citations
Vilmar Asoy is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Prognostics & Computer science. The author has an hindex of 3, co-authored 7 publications. Previous affiliations of Vilmar Asoy include Aalesund University College.
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Papers
A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships
TL;DR: This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems and provides inspiration toward the PHM based on DL in autoships and the maritime industry.
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Fault Prognostics Using LSTM Networks: Application to Marine Diesel Engine
TL;DR: In this paper, the feasibility of applying data-driven fault prognostics to marine diesel engines is investigated, and the proposed method generalizes well on the second profile and provides remaining useful life predictions with high accuracy.
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Virtual prototyping system for maritime crane design and operation based on functional mock-up interface
Yingguang Chu,Lars Ivar Hatledal,Filippo Sanfilippo,Vilmar Asoy,Houxiang Zhang,Hans Georg Schaathun +5 more
- 18 May 2015
TL;DR: This paper presents the framework of a virtual prototyping system for the design and simulation of maritime crane operations by combining the rapid-prototyping approach with the concept of interchangeable interfaces, allowing for the reduction of lead-times and the abatement of mistakes or system failures that may otherwise cause fatal accidents in real tests.
Green ballast water treatment utilizing waste heat recovery
Yanran Cao,Vilmar Asoy,Qin Liang +2 more
- 10 Apr 2016
TL;DR: In this paper, the authors presented a green and cost-effective ballast water treatment system which will utilize waste heat from propulsion machinery for the destruction of microorganisms in the water in a relatively short time.
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Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation
Andre Listou Ellefsen,Xu Cheng,Finn Tore Holmeset,Vilmar Asoy,Houxiang Zhang,Sergey Ushakov +5 more
- 01 Aug 2019
TL;DR: An unsupervised reconstruction-based fault detection algorithm is used to predict faults automatically in a simulated autonomous ferry crossing operation and suggests that the algorithm achieves the highest prediction accuracy when the input data is subjected to feature selection based on sensitivity analysis.