Gaussian process latent variable model and Bayesian inference for non-parametric failure modeling applied to ship engine
Bibliographic record
Abstract
Unnecessary early maintenance is especially critical for high-value or essential components whose unexpected failures could disrupt the entire operational process of the system. The uncertainties inherent in facility deterioration necessitate a robust framework that accurately assesses system health and guides optimal maintenance scheduling. To this end, this paper proposes a probabilistic machine learning framework based on a Gaussian Process Latent Variable Model (GPLVM) combined with Bayesian Inference (BI) to dynamically assess the health state of system and predict failure risk. The model integrates uncertainty quantification through BI, providing a non-parametric hazard rate estimate at each time step, which enables a precise and adaptive maintenance planning strategy. To verify the proposed model, a critical component of an engine – spark ignition, is considered as the case study. Herein, ignition voltage is monitored as the primary indicator of spark health, with degradation thresholds and safety thresholds explicitly modeled to capture degradation trends accurately. The results indicate that 96.5% of the observations fell within precise predictive range (according to Pareto Diagnostics values), underscoring the model’s promise for maintenance planning. This approach has the potential not only to improve predictive accuracy and decision confidence but can also provide a flexible, non-parametric solution adaptable to various high-stakes maintenance applications.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".