Maintenance contract assessment for aging systems
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
Abstract This paper considers an aging system, where the system failure rate is known to be an increasing function. After any failure, maintenance is performed by an external repair team. Repair rate and cost of repair are determined by a corresponding maintenance contract with a repair team. There are many different maintenance contracts suggested by the service market to the system owner. In order to choose the best maintenance contract, a total expected cost during a specified time horizon should be evaluated for an aging system. In this paper, a method is suggested based on a piecewise constant approximation for the increasing failure rate function. Two different approximations are used. For both types of approximations, the general approach for building the Markov reward model is suggested in order to assess lower and upper bounds of the total expected cost. Failure and repair rates define the transition matrix of the corresponding Markov process. Operation cost, repair cost and penalty cost for system failures are taken into account by the corresponding reward matrix definition. A numerical example is presented in order to illustrate the approach. Copyright © 2008 John Wiley & Sons, Ltd.
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 | 0.001 | 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