Condition Indicator-Based Gaussian Process RegressorWith Automatically Labelled Images
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Abstract
The IJPHMNA 2026 data challenge aimed to predict pitting progression in gears, where the two main encountered difficulties were the lack of ground truth, with only images of the tooth flanks provided, requiring further processing for a label to be obtained, and the limited amount of given image acquisitions during training which required a model that could be trained on a low amount of labeled data.
How to Cite
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Data Challenge
Liao, W., & De Geest, R. (2024, June). Active learning for gear defect detection in gearboxes. PHM Society European Conference, 8(1), 10. doi: 10.36001/phme.2024.v8i1.4050
Walluk, M., Tucker, J., Hood, A., Horney, P., Jones, A., Matthews, W., & Nenadic, N. (2025, October). Characterizing surface-damage progression of spur gears with vibration and oil data. Annual Conference of the PHM Society, 17(1). doi: 10.36001/phmconf. 2025.v17i1.4363
Zhu, R., Van Maele, D., Poletto, J. C., De Baets, P., & Gryllias, K. (2024). Development and evaluation of vibration indicators for gear pitting detection. In Proceedings of the international conference on noise and vibration engineering (isma 2024) and international conference on uncertainty in structural dynamics (usd 2024) (pp. 508–521). Leuven, Belgium: KU Leuven, Department of Mechanical Engineering.
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