Maintenance Effectiveness Estimation with Applications to Railway Industry
Bibliographic record
Abstract
We propose a method of estimating the maintenance efficiency and investigating how the efficiency impacts the system's health indicator. We assume power law (Weibull) form of the time-dependent portion of failure intensity and the Cox proportional hazards assumption for the covariate portion of failure intensity. We model the effects of preventive and corrective maintenance as multiplicative on both time and either health indicator, or raw observed covariates. Thus, we incorporate both time- and covariate-dependent parts, as well as the maintenance effects in one failure intensity function. The effects of preventive or corrective types of maintenance are modelled as different maintenance effect parameters. We derive the likelihood function and obtain the least squares estimates (LSE) of covariate coefficient(s), Weibull shape and scale parameters, as well as the preventive and corrective maintenance effect estimates on time and covariate(s). The application of the proposed model is shown in a real case study of railway point machines subject to periodic preventive maintenance and corrective maintenance on failures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".