Data augmentation for forecasting industrial aging processes via conditional multimodal generative time-series models
Bibliographic record
Abstract
Data augmentation has shown to be effective for improving generalization performance of deep neural networks, especially in the regime of high noise and scarce data. However, this approach has not been applied to industrial aging processes (IAP) forecasting, where observed data are multimodal time-series, and therefore existing augmentation methods are not suitable for data generation. In this paper, we propose Seq-MVAE, a generative architecture that can generate complex time-series data consisting of multiple heterogeneous modalities. Seq-MVAE is capable of conditional generation, i.e., Seq-MVAE learns the joint distribution across the modalities, and allows users to generate a part of the modalities that are coherent with the other (given) modalities. This enables not only missing value imputation but also conditional generation, which is known to be crucial for data augmentation. We evaluate the generative performance and other aspects of Seq-MVAE on an artificial dataset generated based designed to simulate an industrial aging process, and show the effectiveness of data augmentation by Seq-MVAE on a real-world dataset acquired from an industrial plant. • Data augmentation is a powerful tool when dealing with scarce and noisy data. • A generative model is introduced that accurately models industrial aging processes. • The model can effectively learn even when training data contains many missing values. • Data augmentation using the model greatly improves forecasting for industrial data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".