Size Estimation of Flaking in Rolling Bearings Using Deep Learning with Explainability
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Abstract
To improve the availability of rotating machines such as wind turbines, where rolling bearing replacement is costly and time-consuming, it is effective to estimate the damage progression of the rolling bearings. As one of the damage progressions, the size of flaking in rolling bearings is estimated by vibration analysis using rule-based methods. However, these rule-based methods require expert knowledge of rolling bearings. Therefore, an estimation model using deep learning was proposed and its performance was evaluated. Furthermore, it was verified that the proposed model had extracted the features of physical phenomena using Grad-CAM.
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Explainability, Machine Learning, Rolling Bearing
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