Using GenRel for reliability assessment of mining equipment
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
Purpose The purpose of this paper is to formulate, develop and test a reliability assessment model (GenRel) based on genetic algorithms. Design/methodology/approach Using genetic algorithm based modelling technique, a computer model was developed to predict mine equipment failures from historical data. Two different approaches in application of this technique are demonstrated. Findings A case study representing a test for convergence of the model was successfully performed. This is an indicator that GenRel can be used to predict equipment failures using a genetic algorithm based modeling technique. Practical implications The use of classical statistical techniques has proven to be an effective tool for reliability analysis of mining equipment. This paper presents an efficient alternative to these classical probability based reliability analysis methods. GenRel is a software solution which performs predictive reliability based upon genetic algorithms (GAs). The advantage of using this technique is the fact that the assumptions based on GAs are much simpler compared to classical statistical methods. The computer model is developed to accept a variety of user input data, most importantly, the ability to use real life historical data in the form of Time Between Failures (TBFs) or Time To Repair (TTRs). Originality/value The proposed research offers an alternative method to conventional statistically based reliability analysis and may lead to the foundation of a new approach for reliability assessment with potential applications in other industrial fields as well.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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