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AI-assistance for predictive maintenance of renewable energy systems

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  • Shin, Won
  • Han, Jeongyun
  • Rhee, Wonjong

Abstract

Although promising results of high-performance AI algorithms have been reported in recent predictive maintenance researches, most of the existing studies merely deal with AI-only solutions and do not consider the interaction between humans and AI. In this study, we explicitly focus on the benefits of interactions where a human inspector is assisted by AI solutions. A case study is conducted for predictive maintenance of wind farms, where endoscopic images were used for bearing fault detection. The experiment consisted of 54 technical inspectors and 2301 images collected over 138 wind turbines, and each inspector was shown images and asked to identify bearing faults in the absence and presence of AI-assistance. The results showed that AI-assistance had a statistically significant impact on improving the technical inspector’s specificity and time efficiency. The level of improvement was dependent on the level of expertise, where the generalist group showed greater improvements in specificity and time efficiency (24.6% and 25.3%, respectively) when compared with the specialist group (4.7% and 6.4%, respectively). Both groups responded positively on the reuse intention and usefulness of AI-assistance, and the change in cognitive load was not statistically significant.

Suggested Citation

  • Shin, Won & Han, Jeongyun & Rhee, Wonjong, 2021. "AI-assistance for predictive maintenance of renewable energy systems," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544221000244
    DOI: 10.1016/j.energy.2021.119775
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    References listed on IDEAS

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    Cited by:

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    3. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    4. Anna Kwiotkowska & Bożena Gajdzik & Radosław Wolniak & Jolita Vveinhardt & Magdalena Gębczyńska, 2021. "Leadership Competencies in Making Industry 4.0 Effective: The Case of Polish Heat and Power Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    5. He, Huizi & Sun, Mei & Li, Xiuming & Mensah, Isaac Adjei, 2022. "A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features," Energy, Elsevier, vol. 244(PA).
    6. Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled & Benkraien, Ramzi, 2022. "Quantile co-movement and dependence between energy-focused sectors and artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    7. Toubeau, Jean-François & Pardoen, Lorie & Hubert, Louis & Marenne, Nicolas & Sprooten, Jonathan & De Grève, Zacharie & Vallée, François, 2022. "Machine learning-assisted outage planning for maintenance activities in power systems with renewables," Energy, Elsevier, vol. 238(PC).

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