Operational Cycle Detection for Mobile Mining Equipment: An Integrative Scoping Review with Narrative Synthesis

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.