Operational Cycle Detection for Mobile Mining Equipment: An Integrative Scoping Review with Narrative Synthesis
Bibliographic record
Abstract
Background: Accurate operational cycle detection underpins maintenance, production analytics and energy management for mobile equipment in mining. Yet no review has investigated the landscape of operational cycle detection literature in mining. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses, Scoping Review extension (PRISMA-ScR) framework, we searched the Lens database on June 27, 2025, for records published 2000–2025 that segment mobile mining vehicle telemetry into discrete operating modes. After de-duplication (n = 1,757) and two-stage screening, 20 empirical studies met all criteria (19 diesel, 1 battery-electric). Due to the sparse research involving battery electric vehicles (BEVs) in mining, three articles performing cycle detection on heavy-duty vehicles in a similar operational context to mining are synthesized. A bespoke three-axis Transferability Lens—created to measure cross-domain applicability of modelling approaches—was applied to four expert-selected passenger BEV studies to investigate cross-domain synthesis. Results: Early diesel work used single-sensor thresholds, often achieving >90\% site-specific accuracy, while recent studies increasingly employ types of neural networks using multivariate datasets. While the cycle detection research on mining BEVs, even supplanted with additional heavy-duty BEV studies, is sparse, similar approaches are favoured. The transferability appraisal suggests only moderate sensor-mapping and retraining effort when adapting automotive BEV classifiers to mining vehicle cycle detection. Conclusions: Persisting gaps in the literature include the absence of public mining datasets, inconsistent evaluation metrics, and limited real-time validation.
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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 | 0.037 | 0.152 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.033 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".