An Advanced Diagnostic Model for Gearbox Degradation Prediction Under Various Operating Conditions and Degradation Levels
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Abstract
This study introduces a novel three-stage diagnostic methodology aimed at enhancing the prediction and classification of gearbox degradation under various operating conditions and multiple degradation levels, addressing the complexities encountered in real-world industrial settings. Leveraging the latest advancements in data-driven approaches, from similarity-based methods to residual-based deep convolutional neural networks (CNNs) and pseudo-labeling techniques, our approach systematically classifies data into known, unknown, and undetermined categories, predicts known degradation levels, and refines classification models with augmented pseudo-label data. The efficacy of our methodology is demonstrated through its remarkable performance using the data from the PHM North America 2023 Conference Data Challenge. It achieves scores of 600 / 800 on the testing data and 574 / 813 on the validation data, significantly surpassing the first-place scores of 463.5 and 472 in the competition, respectively, setting a new benchmark in the field of gear fault diagnosis.
How to Cite
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Multimodal Deep Learning, Machine Learning, Gear Fault Diagnosis, Prognostics and Health Management
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