Remaining Useful Life Prediction of Proton Exchange Membrane Fuel Cells Using Genetic Algorithm Based Nonlinear Autoregressive Exogenous Network
Bibliographic record
Abstract
The proton exchange membrane fuel cell (PEMFC) is one of the most promising clean energy sources with characteristics like high energy conversion, no electrolyte leakage, and low operational temperature.However, it is difficult to build a mathematical model because the system consists of a complex nonlinear system.In the meantime, accurate estimation of the remaining useful life (RUL) of fuel cells plays an important role in improving the safety and lifetime of fuel cells.A joint prediction method based on genetic algorithm (GA) and nonlinear autoregressive neural network with external input (NARX) is proposed.The method was designed to predict the RUL of the proton exchange membrane fuel cell.GA is used to optimize the initial weights and biases of the NARX neural network.Then, the historical voltage evolution under rated current conditions is used to train the NARX network, where the trained model is used to predict the voltage evolution under ripple conditions.Integrating the GA-NARX algorithm leads to the improvement of convergence speed and the prediction accuracy of the algorithm.This integrated algorithm obtains better estimation accuracy compared to the NARX network by itself.The proposed method was compared with the genetic algorithm-based backpropagation neural network (GA-BPNN) and genetic algorithm-based time delay neural network (GA-TDNN).The proposed method is validated with the IEEE PHM 2014 Data Challenge dataset and the resultsshowed that the method has better prediction accuracy compared to other ANN algorithms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".