Blar i Publikasjoner fra CRIStin - NTNU på forfatter "Æsøy, Vilmar"
-
Online Fault Detection in Autonomous Ferries: Using fault-type in-dependent spectral anomaly detection
Ellefsen, Andre; Han, Peihua; Cheng, Xu; Holmeset, Finn Tore; Æsøy, Vilmar; Zhang, Houxiang (Peer reviewed; Journal article, 2020)Enthusiasm for ship autonomy is flourishing in the maritime industry. In this context, data-driven Prognostics and Health Management (PHM) systems have emerged as the optimal way to improve operational reliability and ... -
Parameter Identification of Ship Manoeuvring Model Under Disturbance Using Support Vector Machine Method
Wang, Tongtong; Li, Guoyuan; Wu, Baiheng; Æsøy, Vilmar; Zhang, Houxiang (Peer reviewed; Journal article, 2021).Demanding marine operations increase the complexity of manoeuvring. A highly accurate ship model promotes predicting ship motions and advancing control safety. It is crucial to identify the unknown hydrodynamic coefficients ... -
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture
Ellefsen, Andre; Bjørlykhaug, Emil; Æsøy, Vilmar; Ushakov, Sergey; Zhang, Houxiang (Journal article; Peer reviewed, 2018)In recent years, research has proposed several deep learning (DL) approaches to providing reliable remaining useful life (RUL) predictions in Prognostics and Health Management (PHM) applications. Although supervised DL ... -
Soot Investigation on Fish Oil Spray Combustion in a Constant Volume Cell
Malin, Maximilian Christoph; Krivopolianskii, Vladimir; Rygh, Bjørn; Æsøy, Vilmar; Pedersen, Eilif (Journal article; Peer reviewed, 2015)Maritime environmental regulations stipulate lower emissions from the shipping industry. To cope with these rules, improving the combustion processes, make use of cleaner alternative fuels and implement exhaust gas cleaning ... -
Validation of Data-Driven Labeling Approaches Using a Novel Deep Network Structure for Remaining Useful Life Predictions
Ellefsen, Andre; Ushakov, Sergey; Æsøy, Vilmar; Zhang, Houxiang (Journal article; Peer reviewed, 2019)Today, most research studies that aim to predict the remaining useful life (RUL) of industrial components based on deep learning techniques are using piecewise linear (PwL) run-to-failure targets to model the degradation ... -
Virtual prototyping for maritime crane design and operations
Chu, Yingguang; Hatledal, Lars Ivar; Zhang, Houxiang; Æsøy, Vilmar; Ehlers, Sören (Journal article; Peer reviewed, 2018)This paper presents the implementation of the virtual prototyping system for maritime crane design and operations. The study is designed to bridge the following gaps in maritime crane system simulations. First, the virtual ... -
Virtual Prototyping of Maritime Systems and Operations: Applications of Distributed Co-Simulations
Skjong, Stian; Rindarøy, Martin; Kyllingstad, Lars Tandle; Æsøy, Vilmar; Pedersen, Eilif (Journal article, 2017)In this work, we demonstrate the use of co-simulation technology in the maritime industry through four relevant examples of applications based on the outcome of the knowledge-building project Virtual Prototyping of Maritime ... -
Virtual prototyping system for maritime crane design and operation based on functional mock-up interface
Chu, Yingguang; Hatledal, Lars Ivar; Sanfilippo, Filippo; Æsøy, Vilmar; Zhang, Houxiang; Schaathun, Hans Georg (Chapter; Peer reviewed, 2015)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, ... -
The whereabouts of interorganizational learning: a maritime case study
Pareliussen, Bjarne; Giskeødegård, Marte Fanneløb; Æsøy, Vilmar (Peer reviewed; Journal article, 2022)Purpose This paper aims to present the results from a case study that investigated interorganizational learning in a buyer and seller relationship in the context of the maritime industry. This examination emphasized ...