Modeling failure and maintenance effects of a system subject to multiple preventive maintenance types
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Bibliographic record
Abstract
Canada's mining sector contributed $54 billion to its GDP in 2013. Mining operations are an important element of Canada's economy and rely heavily on mobile equipment for the transportation of rock-ore. Failure of mobile equipment, when it is required to be in available state prevents the successful flow of mining operations, and can result in production losses averaging in millions of tons, annually. Consequently, the availability - and by extension, reliability - of mobile equipment have a direct economic impact on mine productivity. A mobile equipment's failures are the greatest contributors to its unavailability - and are observed to occur randomly. Typically, to help diagnose and curb mobile equipment failures, corrective and preventive maintenance policies are implemented. Maintenance personnel are concerned with quantifying the effect of multiple preventive maintenance policies on mobile equipment reliability and availability. Generally, this is performed by modelling the reliability of repairable systems. In most studies, it is assumed that repairable systems are subject to only one type of repair/maintenance, and the effect of repair/maintenance is captured using a single repair factor in an age reduction or intensity reduction model. In this paper, we consider a repairable system whose failures follow a Non-Homogenous Poisson Process, and the system is subject to corrective and several types of preventive maintenance. While the effect of corrective maintenance is minimal, a preventive maintenance may reduce the age of the system effectively. We assume different effects for different preventive maintenance types, and develop the likelihood function to estimate the failure process and preventive maintenance effects, simultaneously. We also derive the conditional reliability and the expected number of failures between two consecutive preventive maintenance types. The proposed methods are applied to a case study of two trucks used in a mining site. The proposed methods provide excellent predictions with the potential of becoming very useful in practice and of leading to further generalizations of repairable systems analyses.
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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 it