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A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions
Department of Industrial Engineering, Dongguan University of Technology, Dongguan, 523808, China.ORCID iD: 0000-0001-7310-5717
School of Creative Design, Dongguan City University, Dongguan, 523419, China.ORCID iD: 0000-0002-9677-0979
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Division of Product Realization, Mälardalen University, 63220, Eskilstuna, Sweden.ORCID iD: 0000-0002-7458-6820
Department of Industrial Engineering, Dongguan University of Technology, Dongguan, 523808, China.
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2023 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 119, article id 105735Article in journal (Refereed) Published
Abstract [en]

Fault diagnosis of wind turbine gearboxes is crucial in ensuring wind farms’ reliability and safety. However, nonstationary working conditions, such as load change or speed regulation, may result in an accuracy deterioration of many existing fault diagnosis approaches. To overcome the issue, this research proposes a nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes using vibration signals. Concretely, we adopt Empirical Mode Decomposition (EMD) to decompose vibration signals into a series of Intrinsic Mode Functions (IMFs). Then, the multi-channel IMFs are fed into a 1D Convolutional Neural Network (CNN) for automatic feature learning and fault classification. Since EMD is a signal processing technique requiring no prior knowledge, the model architecture can be viewed as nearly end-to-end. The proposed approach was validated in a real-world dataset; it proved deep learning models have an overwhelming advantage in representation capacity over traditional shallow models. It also demonstrated that the introduction of EMD as a preprocessing step improves both the training efficiency and the generalization ability of a deep model, thus leading to a better fault diagnosis efficacy under variable working conditions.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 119, article id 105735
Keywords [en]
Fault diagnosis, Deep learning, End-to-end learning, Empirical mode decomposition, Convolutional neural network
National Category
Other Mechanical Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-95176DOI: 10.1016/j.engappai.2022.105735ISI: 000912326600001Scopus ID: 2-s2.0-85144823492OAI: oai:DiVA.org:ltu-95176DiVA, id: diva2:1724329
Note

Validerad;2023;Nivå 2;2023-03-21 (joosat);

Funder: National Natural Science Foundation of China (71801045); DGUT, China (GC300502-46); Department of Education of Guangdong in China (2021ZDJS083)

Available from: 2023-01-05 Created: 2023-01-05 Last updated: 2023-04-21Bibliographically approved

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Lin, Jing

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