Software architecture for condition monitoring of mobile underground mining machinery: A framework extensible to intelligent signal processing and analysis
Bibliographic record
Abstract
In the mining sector, there are growing calls for the ability to monitor the health of operational assets like structures, mobile underground machinery, and complex stationary equipment. This work focuses on the development of a software architecture to monitor the entire range of industrial and mining equipment - all the while acknowledging the more generic problem of intelligent signal processing having applicability to a much broader class of problem. The implementation of condition monitoring for mobile underground mining equipment relies on previous work by the authors in advancing condition-monitoring techniques for vari able speed and load machinery. The design will permit flexible run-time system configuration with a variety of choices in signal processing and intelligent analysis techniques; it has been demonstrated to work well with an implementation in MATLAB object-oriented programming (OOP) from data collected on a real gearbox subject to varying loads and speeds. This success justifies the advancement of a full-fledged prototype in LabVIEW OOP. The aim is the development of the data analysis layer; the result should integrate seamlessly with such developing industrial standards as the International Rock Excavation Data Exchange Standard (IREDES).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".