Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes
Gearboxes are critical mechanical components in various modern constructions, including wind turbines, making their real-time monitoring and the prevention of major failures essential. Machine learning (ML) offers a precise and robust method for early-stage failure detection and efficient gear monit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/14/3630 |
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| _version_ | 1849246560952516608 |
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| author | Bartłomiej Kiczek Michał Batsch |
| author_facet | Bartłomiej Kiczek Michał Batsch |
| author_sort | Bartłomiej Kiczek |
| collection | DOAJ |
| description | Gearboxes are critical mechanical components in various modern constructions, including wind turbines, making their real-time monitoring and the prevention of major failures essential. Machine learning (ML) offers a precise and robust method for early-stage failure detection and efficient gear monitoring during operation, with computational efficiency that allows for use on edge devices. This article presents a method for detecting surface damage on gear teeth using unsupervised machine learning. Using only experimentally measured vibrational signals from a healthy gearbox as a training set, novel neural network architectures, including convolutional and recurrent autoencoders, were employed and compared with a classical dense autoencoder. The study confirmed the effectiveness of these methods in gear transmission diagnostics and demonstrated the potential for achieving high-quality classification metrics using unsupervised learning. |
| format | Article |
| id | doaj-art-6cf8186340054eefa8e78ab89bf747a2 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-6cf8186340054eefa8e78ab89bf747a22025-08-20T03:58:27ZengMDPI AGEnergies1996-10732025-07-011814363010.3390/en18143630Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine GearboxesBartłomiej Kiczek0Michał Batsch1Department of Quantitative Methods in Management, Lublin University of Technology, 20-618 Lublin, PolandDepartment of Mechanical Engineering, Rzeszów University of Technology, 35-959 Rzeszów, PolandGearboxes are critical mechanical components in various modern constructions, including wind turbines, making their real-time monitoring and the prevention of major failures essential. Machine learning (ML) offers a precise and robust method for early-stage failure detection and efficient gear monitoring during operation, with computational efficiency that allows for use on edge devices. This article presents a method for detecting surface damage on gear teeth using unsupervised machine learning. Using only experimentally measured vibrational signals from a healthy gearbox as a training set, novel neural network architectures, including convolutional and recurrent autoencoders, were employed and compared with a classical dense autoencoder. The study confirmed the effectiveness of these methods in gear transmission diagnostics and demonstrated the potential for achieving high-quality classification metrics using unsupervised learning.https://www.mdpi.com/1996-1073/18/14/3630gearsdeep learningautoencodersneural networksfault detectionanomaly detection |
| spellingShingle | Bartłomiej Kiczek Michał Batsch Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes Energies gears deep learning autoencoders neural networks fault detection anomaly detection |
| title | Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes |
| title_full | Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes |
| title_fullStr | Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes |
| title_full_unstemmed | Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes |
| title_short | Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes |
| title_sort | exploration of unsupervised deep learning based gear fault detection for wind turbine gearboxes |
| topic | gears deep learning autoencoders neural networks fault detection anomaly detection |
| url | https://www.mdpi.com/1996-1073/18/14/3630 |
| work_keys_str_mv | AT bartłomiejkiczek explorationofunsuperviseddeeplearningbasedgearfaultdetectionforwindturbinegearboxes AT michałbatsch explorationofunsuperviseddeeplearningbasedgearfaultdetectionforwindturbinegearboxes |