Crawler Crane Maintenance Optimization with Increased Reliability Through Preventive and Corrective Maintenance Strategies
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
This research focuses on optimizing the maintenance strategy of a crawler crane to increase reliability through a combination of preventive and corrective maintenance.Operational and failure data were collected and analyzed to identify relevant probability distribution parameters.The results showed that applying optimal preventive maintenance intervals increased the crawler crane's reliability from 36.79% to 90.04%.In addition, the total maintenance cost per incident was successfully reduced from IDR 11,478,182 to IDR 1,870,657.Cumulatively, with the simulations and iterations carried out, the cost reduction carried out can save IDR 86,312,745 crawler crane maintenance costs if carried out with the same total duration of 6,738 hours.Simulations and iterations showed that the optimized maintenance strategy could reduce the risk of failure due to increased reliability and significantly improve the efficiency of maintenance operational costs.This research concluded that maintenance optimization using a probability distribution approach effectively increased reliability and reduced crawler crane maintenance costs.The use of appropriate preventive maintenance intervals has been shown to have a significant impact on reducing component failures and cost efficiency so that crawler crane operations can run more reliably and as planned.
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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 | 0.001 | 0.001 |
| 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