Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes

Bartłomiej Kiczek () and Michał Batsch
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Bartłomiej Kiczek: Department of Quantitative Methods in Management, Lublin University of Technology, 20-618 Lublin, Poland
Michał Batsch: Department of Mechanical Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland

Energies, 2025, vol. 18, issue 14, 1-18

Abstract: 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.

Keywords: gears; deep learning; autoencoders; neural networks; fault detection; anomaly detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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