Long-Term Prediction of Remaining Useful Life for Industrial IoT
Bibliographic record
Abstract
Industrial Internet of Things (IIoT), a branch of the Internet of Things (IoT) for the industrial sector, plays a vital role in integrating industrial equipment, monitoring equipment health, and improving the overall efficiency of industrial production process. Accurately predicting the remaining useful life (RUL) of IIoT equipment is a crucial task in prognostic health management (PHM), which analyzes the degradation trend of industrial equipment to schedule maintenance activi-ties in a timely manner. Artificial Intelligence (AI) techniques, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs), have been widely used in RUL prediction. However, these techniques face challenges in incorporating long-sequence information to capture degradation trends and predicting long-term RUL values. In this paper, we propose an Informer-based method, Co-Informer, for long-term RUL prediction. Co-Informer utilizes a series of sensor data to provide the predicted RUL values during an upcoming time window. In our research, extensive experiments are carried out with C-MAPSS, a widely used turbofan engine degradation dataset provided by NASA. Our experimental results indicate that Co-Informer outperforms the state-of-the-art schemes for RUL prediction in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".