Availability Optimization of the Mobile Crane Using Approach Reliability Engineering at Oil and Gas Company
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
Reliability engineering is needed for scheduling maintenance to improve system or equipment performance. The purpose of this paper is to provide recommendations for maintenance schedules based on reliability, availability, and maintainability (RAM) analysis that optimizes the availability of mobile cranes operating in Indonesian oil and gas companies. This paper begins with searching for a critical system using the Pareto principle. The critical system is then made a reliability block diagram to facilitate the analysis. However, before conducting the analysis, it is necessary to know the characteristics of the probability distribution of the data so that the analysis results are accurate and stable. Analysis of reliability, availability, and maintainability (RAM) is then carried out in the probability distribution equation for each system. The analysis results show that the implementation of maintenance at the time of reliability reaches the mean time between failure, and the maximum maintainability time is 240 minutes. The result is an increase in the availability of the lower structure (LS) system by 0.53% (98.78% to 99.31%), electrical and safety equipment (ESE) by 0.19% (99.02% to 99.21%), upper structure (US) was 0.07% (99.32% to 99.39), and overall system availability was 0.07% (99.31% to 99.38%).
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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 | 0.001 | 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