A novel imbalanced fault diagnosis method based on area identification conditional generative adversarial networks
Bibliographic record
Abstract
Abstract As a vital technology for ensuring the stable operation of industrial equipment, fault diagnosis has received a lot of research in recent years. Most complex industrial processes are in normal working conditions during operation, so the amount of data collected under normal working conditions is much larger than that under fault working conditions. The uneven number of samples will lead to the imbalance of datasets and make it a challenging task to assure the overall accuracy. To address the issue, an innovative imbalanced fault diagnostic approach based on area identification conditional generative adversarial networks (AICGAN) is proposed. First, considering the imbalance between normal data (majority data) and fault data (minority data), a hybrid data generation method combining over‐sampling and AICGAN generator is proposed, which effectively extends the limited minority data and overcomes the inclination to majority data to some extent. On one hand, the over‐sampling algorithm reduces the impact of dataset imbalance on the AICGAN training process by linear interpolation. On the other hand, the trainable generator can create samples similar to real samples by learning the generation principle so as to enrich the minority data information and reduce the sample stacking caused by linear synthesis. The two sample production methods complement each other. Combining the raw samples, over‐sampled samples, and samples generated by generator, a new dataset is constructed. Second, the new dataset is used to train the AICGAN discriminator. In addition, in order to generate samples with higher value, an auxiliary discrimination layer is added to the discriminator to control the pattern of generated samples. Third, the balanced dataset containing the linear synthesis samples and the samples generated by the trained generator are put into the classifier to obtain the fault diagnosis. The effectiveness of the proposed approach for fault diagnosis based on AICGAN is verified using the three‐phase flow facility (TFF) dataset and the Tennessee Eastman (TE) dataset. The experimental results demonstrate that the AICGAN‐based fault diagnosis method achieves high F1 scores on the imbalanced dataset.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".