David Flynn — Heriot-Watt University

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Co Author Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2005 2019

42 Conference contribution
31 Article
26 Paper
4 Commissioned report
12 More

Blockchain technology in the energy sector: A systematic review of challenges and opportunities

Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D. P., McCallum, P. & Peacock, A., Feb 2019, In : Renewable and Sustainable Energy Reviews. 100, p. 143-174 32 p.

Research output: Contribution to journal › Article

Open Access

File

Industry

Electric vehicles

Parallel algorithms

Innovation

Internet of things

Certification of Safe and Trusted Robotic Inspection of Assets

Dinmohammadi, F., Page, V., Flynn, D., Robu, V., Fisher, M., Patchett, C., Jump, M., Tang, W. & Webster, M., 7 Jan 2019, 2018 Prognostics and System Health Management Conference (PHM-Chongqing). IEEE, p. 276-284 9 p. (Prognostics and System Health Management Conference (PHM-Chongqing)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access

File

Robotics

Inspection

Robots

Health

Bayesian networks

Extreme environment interconnects and packaging for power electronics

Campos-Zatarain, A., Hinton, J., Mirgkizoudi, M., Li, J., Harris, R., Kay, R. W. & Flynn, D., 15 Apr 2019, In : Journal of Engineering.

Research output: Contribution to journal › Conference article

Open Access

File

Power electronics

Packaging

Interdiffusion (solids)

Substrates

Printing

Integrity Analysis Inspection and Lifecycle Prediction of Subsea Power Cables

Tang, W., Brown, K. E., Flynn, D. & Pellae, H., 7 Jan 2019, 2018 Prognostics and System Health Management Conference (PHM-Chongqing) . Ding, P., Li, C., Yang, S., Ding, P. & Sanchez, R-V. (eds.). IEEE, p. 105-114 10 p. (Prognostics and System Health Management Conference (PHM-Chongqing)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access

File

Cables

Inspection

Sonar

Failure modes

Monitoring

Machine learning methods for wind turbine condition monitoring: A review

Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J. & Nenadic, G., Apr 2019, In : Renewable Energy. 133, p. 620-635 16 p.

Research output: Contribution to journal › Article

Open Access

File

Condition monitoring

Wind turbines

Learning systems

Feature extraction

Decision trees