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A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions
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- Zhang, L. (author)
- Department of Industrial Engineering, Dongguan University of Technology, Dongguan, 523808, China
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- Fan, Q. (author)
- School of Creative Design, Dongguan City University, Dongguan, 523419, China
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- Lin, Jing (author)
- Luleå tekniska universitet,Mälardalens universitet,Innovation och produktrealisering,Division of Operation and Maintenance, Luleå University of Technology, Luleå, 97187, Sweden,Drift, underhåll och akustik,Division of Product Realization, Mälardalen University, 63220, Eskilstuna, Sweden
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- (creator_code:org_t)
- Elsevier Ltd, 2023
- 2023
- English.
- In: Engineering applications of artificial intelligence. - : Elsevier Ltd. - 0952-1976 .- 1873-6769. ; 119
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Annan maskinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Other Mechanical Engineering (hsv//eng)
Keyword
- Convolutional neural network
- Deep learning
- Empirical mode decomposition
- End-to-end learning
- Fault diagnosis
- Convolution
- Convolutional neural networks
- Deterioration
- Failure analysis
- Fault detection
- Intrinsic mode functions
- Wind power
- Wind turbines
- Condition
- End to end
- Faults diagnosis
- Learning approach
- Vibration signal
- Wind turbine gearboxes
- Drift och underhållsteknik
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- ref (subject category)
- art (subject category)
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