Reconstruction error‐based fault detection of time series process data using generative adversarial auto‐encoders
Bibliographic record
Abstract
Abstract Faults in time series process data are typically difficult to detect due to the complex temporal correlations of data samples. In this context, traditional unsupervised machine learning algorithms, such as principal component analysis, independent component analysis, and so forth, would yield only limited performance. Deep learning‐based methods have been employed in recent years to address these problems. Recently, generative adversarial networks have emerged as a promising generative modelling approach for learning data distributions. Inspired by the above, in this study, we present a novel reconstruction error‐based fault detection method for time series process data using generative adversarial auto‐encoder (GAAE) with Wasserstein loss, cycle consistency loss, and gradient penalty methods. The proposed method is designed to detect abnormal patterns in the time series data by training a GAAE to learn the underlying normal behaviour of the data. GAAEs help in effectively capturing the data's hidden distribution, and Wasserstein loss with gradient penalty is used to improve the accuracy of the latent space representation of the data, while the cycle consistency loss ensures consistency between the input and output data during the reconstruction process. Based on the extent of the reconstruction error metric of the GAAEs, we identify the potential faults in the process data stream. The proposed method is evaluated on two data process sets, namely, the Tennessee Eastman benchmark process dataset and the nuclear power flux real‐time dataset from a pressurized heavy water nuclear reactor, to validate the efficacy of the proposed approach.
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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 | 0.005 |
| 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) | 0.001 | 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".