Fuel Cell Life Prediction Model Based on CNN-Attention
Bibliographic record
Abstract
Remaining useful life (RUL) prediction of fuel cells is a topic of significant research attention. This paper presents a convolutional neural network (CNN)-attention hybrid model for predicting the RUL of fuel cells. We used road data to verify the predictive capabilities of the model and compared it with the long short-term memory (LSTM) model, CNN model, and attention model. The results showed that the CNN-attention hybrid model can effectively combine the local feature extraction capability of CNNs and global feature capture capability of attention to fully extract high-accuracy voltage attenuation information from the input data. At a training ratio of 70%, the mean absolute percentage error was 17% lower than that of the LSTM model, 25% lower than that of the CNN model, and 24% lower than that of the attention model. In terms of the stability of the model output results, the maximum deviation of four independent predictions did not exceed 0.5%, indicating that the model outputs have high stability. We further analyzed the long-term prediction performance of the model, and the results showed that the hybrid model can achieve effective long-term RUL prediction of fuel cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".