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
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. 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.
Article Content
From source RSS / original summaryarXiv: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. This paper addresses this gap for an angle grinder by combining uncertainty-aware functional prediction with component-level fatigue assessment in an instance-specific reliability workflow. The proposed framework combines the current tool state with recent force--torque usage windows.
A convolutional encoder extracts loading patterns from spindle forces and shaft torque, and an LSTM backbone predicts nine functional variables as Gaussian mean and variance estimates. In parallel, the same loading history is translated into output-shaft fatigue information through finite-element-supported stress reconstruction, S--N/Miner damage evaluation with Haibach extension, and Paris-law crack-growth analysis.
A streaming replay algorithm consolidates both branches into functional, material, and system reliability trajectories. Held-out tests show mean \(2\%\)-tolerance accuracy of 0. 9652 across nine outputs. Thermal variables are predicted near-perfectly, while drive motor current and load speed remain the most demanding dynamic outputs, with \(R^2\) values of 0. 9750 and 0. 9924.
Torque history is especially important for these variables, and the conventional LSTM outperforms GRU and xLSTM in the short-history setting. Reliability calibration is most informative for drive motor current, where predicted and observed exceedance probabilities ...
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