Iraklis Lazakis
Iraklis Lazakis
Professor in Maritime Operations and Maintenance, NAOME, University of Strathclyde
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Cited by
Cited by
Sensitivity analysis of offshore wind farm operation and maintenance cost and availability
R Martin, I Lazakis, S Barbouchi, L Johanning
Renewable Energy 85, 1226-1236, 2016
Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study
C Gkerekos, I Lazakis, G Theotokatos
Ocean Engineering 188, 106282, 2019
Advanced logistics planning for offshore wind farm operation and maintenance activities
Y Dalgic, I Lazakis, I Dinwoodie, D McMillan, M Revie
Ocean Engineering 101, 211-226, 2015
Availability, operation and maintenance costs of offshore wind turbines with different drive train configurations
J Carroll, A McDonald, I Dinwoodie, D McMillan, M Revie, I Lazakis
Wind Energy 20 (2), 361-378, 2017
Predicting ship machinery system condition through analytical reliability tools and artificial neural networks
I Lazakis, Y Raptodimos, T Varelas
Ocean Engineering 152, 404-415, 2018
Development of a combined operational and strategic decision support model for offshore wind
I Dinwoodie, D McMillan, M Revie, I Lazakis, Y Dalgic
Energy Procedia 35, 157-166, 2013
Increasing ship operational reliability through the implementation of a holistic maintenance management strategy
I Lazakis, O Turan, S Aksu
Ships and Offshore Structures 5 (4), 337-357, 2010
Advanced ship systems condition monitoring for enhanced inspection, maintenance and decision making in ship operations
I Lazakis, K Dikis, AL Michala, G Theotokatos
Transportation Research Procedia 14, 1679-1688, 2016
Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision-making environment
I Lazakis, A Ölçer
Proceedings of the Institution of Mechanical Engineers, Part M: Journal of …, 2016
Maintenance/repair and production-oriented life cycle cost/earning model for ship structural optimisation during conceptual design stage
O Turan, Aİ Ölçer, I Lazakis, P Rigo, JD Caprace
Ships and Offshore Structures 4 (2), 107-125, 2009
Machine learning and data-driven fault detection for ship systems operations
M Cheliotis, I Lazakis, G Theotokatos
Ocean Engineering 216, 107968, 2020
Marine dual fuel engine modelling and parametric investigation of engine settings effect on performance-emissions trade-offs
S Stoumpos, G Theotokatos, E Boulougouris, D Vassalos, I Lazakis, ...
Ocean Engineering 157, 376-386, 2018
Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications
Y Raptodimos, I Lazakis
Ships and Offshore Structures 13 (6), 649-656, 2018
A novel, data-driven heuristic framework for vessel weather routing
C Gkerekos, I Lazakis
Ocean Engineering 197, 106887, 2020
Real-time data-driven missing data imputation for short-term sensor data of marine systems. A comparative study
C Velasco-Gallego, I Lazakis
Ocean Engineering 218, 108261, 2020
Investigation of optimum crew transfer vessel fleet for offshore wind farm maintenance operations
Y Dalgic, I Lazakis, O Turan
Wind Engineering 39 (1), 31-52, 2015
Vessel charter rate estimation for offshore wind O&M activities
Y Dalgic, I Lazakis, O Turan
International Maritime Association of Mediterranean IMAM 2013, 899-907, 2013
Investigating an SVM-driven, one-class approach to estimating ship systems condition
I Lazakis, C Gkerekos, G Theotokatos
Ships and Offshore Structures 14 (5), 432-441, 2019
Investigation of optimum jack-up vessel chartering strategy for offshore wind farm O&M activities
Y Dalgic, I Lazakis, O Turan, S Judah
Ocean Engineering 95, 106-115, 2015
Application of NARX neural network for predicting marine engine performance parameters
Y Raptodimos, I Lazakis
Ships and Offshore Structures 15 (4), 443-452, 2020
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