Enhance Mining System Reliability through System Integration Approach
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Bibliographic record
Abstract
Enhance Mining System Reliability through System Integration Approach Y. Sun, X.S. Li, H. Guo Pages 555-563 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: The reliability of mining systems is generally low due to their harsh working conditions. Currently, efforts for improving mining system reliability are often made in isolation. This practice could substantially limit the effectiveness of the efforts on overall reliability improvement of the mining system. To enhance the overall reliability of mining systems, an integrated improvement approach is necessary. In this paper, we developed a framework for integrated mining system reliability improvement to address this issue. In this framework, there are five major components including data integration, business process integration, hardware integration, software integration, and analysis/decision integration, but we only focus on the integrated reliability analysis which is important to the analysis/decision integration. The reliability analysis considers the interactions between machines, and the impacts of design, operation, maintenance, automation and working environment on the overall system reliability. These multiple interactions present a big challenge to accurate reliability prediction. In this paper, we for the first time systematically investigated integrated reliability analysis approaches for dealing with this challenge using novel models and methods, including covariate hazard models, intelligent reliability prediction approach, and complex system modeling methods. While these models and methods have found some successful applications in other industries, they in general have not been effectively used for the reliability analysis of mining systems. Our study results show that the system integration approach is applicable to mining systems and can be used for developing a computer aided integration system for the implementation of the integrated reliability improvement approach. Keywords: Reliability, mining system, integration, failure rate, safety, productivity DOI: https://doi.org/10.22260/ISARC2013/0060 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 |
| 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