Maintenance 4.0 in Mining Trucks: Data Digitalization and Advanced Protocols
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
Mobile mining equipment is essential but susceptible to costly failures. Despite the critical role of predictive maintenance in preventing such failures, existing research often lacks comprehensive frameworks that effectively integrate real-time telemetry data with advanced analytical tools to support decision-making in challenging mining environments. This study, conducted in collaboration with a mining partner, introduces a maintenance 4.0 approach utilizing telemetry data to enhance equipment maintenance and lifecycle management, with a focus on sustainable production. The methodology includes digitizing maintenance data and creating interactive dashboards with Power BI. Machine learning and statistical models enable real-time analysis of telemetry data to detect early failure signs, improving predictive maintenance. Integrating these technologies enhances equipment availability, optimizes maintenance costs, and improves operator health and safety. This approach promotes a digital, intelligent supply chain, fostering efficient and sustainable operations and enhancing the reliability of mining activities in challenging environments.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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