Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory
Quick Answer
This paper presents a novel framework for predicting functional behavior and assessing material fatigue in angle grinders, achieving a mean accuracy of 0.9652 across nine outputs.
Quick Take
By integrating uncertainty-aware predictions with component-level fatigue analysis, the approach enhances reliability assessments under varying operational conditions, particularly excelling in predicting thermal variables and drive motor current.
Key Points
- Combines functional prediction with component-level fatigue assessment for angle grinders.
- Achieves 0.9652 mean accuracy across nine functional outputs in held-out tests.
- Utilizes convolutional encoders and LSTM for extracting loading patterns and predictions.
- Thermal variables predicted with near-perfect accuracy; drive motor current remains challenging.
- Reliability calibration is crucial for accurate predictions of drive motor current exceedance.
Paper Resources
Source Excerpt
arXiv:2606. 05334v1 Announce Type: new Abstract: Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cannot be decided from the current inspection alone, because future function fulfillment and component integrity may evolve differently under the next service scenario.
Existing PHM approaches support degradation prediction, but often target fixed operating conditions or isolated component benchmarks, while material-fatigue assessment is rarely linked to system-level functional prognosis. …
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