A Robust LPV-ARMA Model With Generalized Maximum Correntropy Criterion for Gearbox Fault Detection Under Non-Stationary and Non-Gaussian Conditions
Bibliographic record
Abstract
Abstract The linear parameter varying autoregressive moving average (LPV-ARMA) model is a powerful tool for analyzing non-stationary time series. However, conventional LPV-ARMA models typically rely on mean square error (MSE) minimization for parameter estimation, making them highly sensitive to non-Gaussian noise, especially in the presence of random impulsive disturbances. This limitation can significantly degrade modeling accuracy and fault detection performance. In this paper, we propose a robust LPV-ARMA model that incorporates the generalized maximum correntropy criterion (GMCC) as an alternative parameter estimation method. GMCC effectively suppresses the influence of impulsive noise by assigning lower weights to extreme deviations, improving the model’s robustness to non-Gaussian disturbances. A comprehensive simulation study is conducted to evaluate the proposed model, demonstrating its superior accuracy in system identification and enhanced fault detection capabilities compared to conventional approaches. The results highlight the effectiveness of GMCC in handling complex, noisy environments.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".