Research output per year
Research output per year
Personal profile
Thematic collaboration
- TU Delft Wind Energy Institute (DUWIND)
Fingerprint
Dive into the research topics where D. Zappalá is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Condition monitoring of wind turbine drivetrains: state-of-the-art technologies, recent trends, and future outlook
Kestel, K., Chesterman, X., Zappalá, D., Watson, S., Hart, E., Carroll, J., Vidal, Y., Nejad, A. R., Sheng, S. & More Authors, 2026, In: Wind Energy Science. 11, 6, p. 2103-2155 53 p.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile -
Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning
De Florio, M., Appleby, G., Keller, J., Eftekhari Milani, A., Zappalá, D. & Sheng, S., 2026, In: Wind Energy Science. 11, 3, p. 737–752 16 p.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus)2 Downloads (Pure) -
Infrared-based damage detection in thick composites via transfer learning on simulated and experimental data
Li, M., Leonetti, D., Zappalá, D. & Snijder, H. H., 2026, In: Composites Part B: Engineering. 309, 18 p., 113112.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile -
A hybrid Convolutional Autoencoder training algorithm for unsupervised bearing health indicator construction
Milani, A. E., Zappalá, D. & Watson, S. J., 2025, In: Engineering Applications of Artificial Intelligence. 139, 16 p., 109477.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile14 Link opens in a new tab Citations (Scopus)93 Downloads (Pure) -
Sensitivity of Drivetrain Condition Monitoring Signals to Wind Turbine Blade Damage
Watson, S. J., Pal, S. K., Zappalá, D. & Nejad, A. R., 2025, In: Journal of Physics: Conference Series. 3025, 1, 12 p., 012012.Research output: Contribution to journal › Conference article › Scientific › peer-review
Open AccessFile
Datasets
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Data supporting the publication: A hybrid Convolutional Autoencoder training algorithm for unsupervised bearing health indicator construction
Eftekhari Milani, A. (Creator), Zappalá, D. (Creator) & Watson, S. J. (Creator), TU Delft - 4TU.ResearchData, 23 Apr 2026
DOI: 10.4121/5B9C4942-40A2-4968-B81A-626F0322EECF
Dataset/Software: Dataset
Activities
- 1 Talk or presentation at a workshop, seminar, course or other meeting
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Optimization of Wind Farm Maintenance: Reliability and Condition Monitoring
Zappalá, D. (Speaker)
8 Jul 2021Activity: Talk or presentation › Talk or presentation at a workshop, seminar, course or other meeting