A maintenance model with minimal and general repair
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this article is to determine optimal maintenance policies for deteriorating systems subject to failure. Complex systems that deteriorate with usage and age are often subject to random failures of several kinds. Since it is costly to repair or replace failed systems, preventive maintenance is usually carried out while the systems are still operational. This article will be useful in providing maintenance engineers with a methodology to model failing systems and efficiently determine optimal maintenance policies. The problem is formulated and solved in a semi-Markov decision framework with the optimality criterion being the minimization of the long-run expected average cost per unit time. The model developed in this article, which is an extension of recent maintenance models, can be applied to systems that have any finite number of major and/or minor failure states and to systems that permit general repair in operational and major failure states. A new computational approach using an embedded technique is developed that is computationally preferable to the standard policy iteration algorithm when determining the optimal maintenance policy for systems with many states.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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