Jannis Tautz-Weinert
Jannis Tautz-Weinert
Verified email at lboro.ac.uk - Homepage
Title
Cited by
Cited by
Year
Using SCADA data for wind turbine condition monitoring - a review
J Tautz-Weinert, SJ Watson
IET Renewable Power Generation, 2016
1952016
Neural networks for wind turbine fault detection via current signature analysis
RK Ibrahim, J Tautz-Weinert, SJ Watson
WindEurope Summit 2016, 2016
252016
Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection
J Tautz-Weinert, SJ Watson
Journal of Physics: Conference Series 753 (7), 072014, 2016
172016
Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements
L Colone, M Reder, J Tautz-Weinert, JJ Melerob, A Natarajana, ...
Energy Procedia 137, 571–578, 2017
112017
Detecting critical scour developments at monopile foundations under operating conditions
J Weinert, U Smolka, B Schumann, PW Chen
Loughborough University, 2015
112015
Statistical evaluation of SCADA data for wind turbine condition monitoring and farm assessment
E Gonzalez, J Tautz-Weinert, JJ Melero, SJ Watson
Journal of Physics: Conference Series 1037 (3), 032038, 2018
102018
Challenges in using operational data for reliable wind turbine condition monitoring
J Tautz-Weinert, SJ Watson
The 27th International Ocean and Polar Engineering Conference, 2017
62017
Sensitivity study of a wind farm maintenance decision-A performance and revenue analysis
J Tautz-Weinert, NY Yürüşen, JJ Melero, SJ Watson
Renewable energy 132, 93-105, 2019
52019
The financial benefits of various catastrophic failure prevention strategies in a wind farm: Two market studies (UK-Spain)
NY Yürüşen, J Tautz-Weinert, SJ Watson, JJ Melero
Journal of Physics: Conference Series 926 (1), 012014, 2017
42017
Condition monitoring of wind turbine drive trains by normal behaviour modelling of temperatures
J Tautz-Weinert, S Watson
Conference for Wind Power Drives, 359, 2017
42017
Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data
D Cevasco, J Tautz-Weinert, AJ Kolios, U Smolka
Journal of Physics: Conference Series 1618 (2), 022063, 2020
22020
Improved wind turbine monitoring using operational data
J Tautz-Weinert
Loughborough University, 2018
22018
Validation of CPT-based initial soil stiffness in sand for offshore wind jacket piles
LBT Bom, SS Siedler, J Tautz-Weinert
Proceedings of the Messen in der Geotechnik, 2020
12020
Combining model-based monitoring and a physics of failure approach for wind turbine fail-ure detection
J Tautz-Weinert, SJ Watson
30th Conference on Condition Monitoring and Diagnostic Engineering …, 2017
12017
Feasibility of machine learning algorithms for classifying damaged offshore jacket structures using SCADA data
D Cevasco, J Tautz-Weinert, U Smolka, A Kolios
Journal of Physics: Conference Series 1669 (1), 012021, 2020
2020
Condition monitoring by neural network modelling of drive train temperature
J Weinert, SJ Watson
12th EAWE PhD Seminar on Wind Energy in Europe, Ankara, 2016
2016
Wind turbine fault de-tection by normal behaviour modelling
J Weinert, SJ WATSON
Midlands Energy Consortium Postgraduate Student Conference, Loughborough …, 2015
2015
Challenges in using operational data for reliable wind turbine condition monitoring
J Weinert, S Watson
Loughborough University, 0
Condition monitoring of wind turbine drive trains by normal behaviour modelling of temperatures
J Weinert, S Watson
Loughborough University, 0
Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection
J Weinert, S Watson
Loughborough University, 0
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