Exploring Kolmogorov–Arnold Networks for Unsupervised Anomaly Detection in Industrial Processes
Bibliographic record
Abstract
Designing reliable fault detection and diagnosis (FDD) systems remains difficult when only limited fault-free data are available. Kolmogorov–Arnold Networks (KANs) have recently been proposed as parameter-efficient alternatives to multilayer perceptrons, yet their effectiveness for unsupervised FDD has not been systematically established. This study presents a statistically grounded comparison of Kolmogorov–Arnold Autoencoders (KAN-AEs) against an orthogonal autoencoder and a PCA baseline using the Tennessee Eastman Process benchmark. Four KAN-AE variants (EfficientKAN-AE, FastKAN-AE, FourierKAN-AE, and WavKAN-AE) were trained on fault-free data subsets ranging from 625 to 250,000 samples and evaluated over 30 independent runs. Detection performance was assessed using Bayesian signed-rank tests to estimate posterior probabilities of model superiority across fault scenarios. The results show that WavKAN-AE and EfficientKAN-AE achieve approximately 90–92% fault detection rate with only 2500 samples. In contrast, the orthogonal autoencoder requires over 30,000 samples to reach comparable performance, while PCA remains markedly below this level regardless of data size. Under data-rich conditions, Bayesian tests show that the orthogonal autoencoder matches or slightly outperforms the KAN-AEs on the more challenging fault scenarios, while remaining computationally more efficient. These findings position KAN-AEs as compact, data-efficient tools for industrial fault detection when historical fault-free data are scarce.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".