A Digital Twin Model Updating Method to Capture Lifecycle System Degradation
Bibliographic record
Abstract
Abstract Iterative update methods are important for digital twin (DT) models to learn from new data and maintain the DT performance during application. However, when considering system lifecycle degradations, current update methods fail to enable DT models to capture system responses affected by degradation over time. To alleviate this problem, degradation models of measurable physical parameters are often integrated into DT construction. This operation is costly since identifying the degradation parameters depends on prior knowledge of the system and requires expensive experiments. To overcome above limitations, this paper proposes a lifelong update method for DT models to capture the effects of degradation on system responses without any prior knowledge and expensive offline experiments on the system. In this work, the DT model is constructed as a feedforward neural network with a fixed structure, and the effect of system degradation is assumed to be reflected by the DT parameters obtained at each degradation stage. During the lifelong update process, an extreme learning machine is adopted to capture how the model parameters change in lifecycle for each layer. The constructed extreme learning machines could predict model parameters of the entire DT model at any future degradation stage. The proposed method is evaluated with the battery degradation dataset from Oxford University. The test results demonstrate that the proposed method could capture effects of system degradation on system responses during the lifecycle and outperform the conventional fine-tuning method in terms of prediction accuracy and robustness at future stages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".