Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study
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- Dahlander, Linus & Gann, David M., 2010. "How open is innovation?," Research Policy, Elsevier, vol. 39(6), pages 699-709, July.
- Saebi, Tina & Foss, Nicolai J., 2015. "Business models for open innovation: Matching heterogeneous open innovation strategies with business model dimensions," European Management Journal, Elsevier, vol. 33(3), pages 201-213.
- Harriman Samuel Saragih & Jacob Donald Tan, 2018. "Co-innovation: a review and conceptual framework," International Journal of Business Innovation and Research, Inderscience Enterprises Ltd, vol. 17(3), pages 361-377.
- Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
- Liu, Yichao & Ferrari, Riccardo & Wu, Ping & Jiang, Xiaoli & Li, Sunwei & Wingerden, Jan-Willem van, 2021. "Fault diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: A mixed model and signal-based approach," Renewable Energy, Elsevier, vol. 164(C), pages 391-406.
- Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
- Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
- Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
- Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
- Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
- André de Waal & Michael Weaver & Tammy Day & Beatrice van der Heijden, 2019. "Silo-Busting: Overcoming the Greatest Threat to Organizational Performance," Sustainability, MDPI, vol. 11(23), pages 1-21, December.
- Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
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- Arranz, Carlos F.A. & Arroyabe, Marta F. & Arranz, Nieves & de Arroyabe, Juan Carlos Fernandez, 2023. "Digitalisation dynamics in SMEs: An approach from systems dynamics and artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
- Sarah Barber & Unai Izagirre & Oscar Serradilla & Jon Olaizola & Ekhi Zugasti & Jose Ignacio Aizpurua & Ali Eftekhari Milani & Frank Sehnke & Yoshiaki Sakagami & Charles Henderson, 2023. "Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation," Energies, MDPI, vol. 16(8), pages 1-23, April.
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Keywords
wind energy; digitalisation; collaboration; co-innovation; machine learning; fault detection;All these keywords.
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