2346 These families allow the representation of both explicit signals and underlying conditions that contribute to the accumulation of potential before the occurrence of high-impact events. A conceptual function is preliminarily defined: QPIFAT = ∑ (Ψᵢ · Wᵢ) Where: • Ψᵢ = Potential state i expressed as a preventative vector. • Wᵢ = Mathematical weight associated with criticality and operational energy. This formulation is conceptual and exploratory in nature, and will be refined, calibrated, and validated during the pilot phase using real data from the fatigue monitoring system. 5. CURRENT CAPABILITIES OF OAS SYSTEMS Operator alertness monitoring and alerting systems (OAS) are currently one of the most widely implemented technological controls for managing the risk associated with fatigue in large-scale mining operations. These systems allow for continuous, real-time monitoring of operator alertness by detecting physiological and behavioral indicators associated with fatigue during equipment operation. The main contribution of OAS systems lies in their ability to identify fatigue events at the individual level, enabling immediate alerts and short-term interventions, consistent with established fatigue management protocols. In this context, OAS acts as a reactive, event-based safety control, supporting operational decision-making the moment detectable fatigue symptoms appear. However, while OAS improves situational awareness at the operational level, its analytical scope remains limited to the operator's current state. The system does not intrinsically assess the broader operational context, cumulative exposure, or latent conditions that may influence system vulnerability before observable indicators of fatigue become apparent. 5.1 Structural Limitations of Event-Based Fatigue Monitoring Despite their operational value, OAS systems have inherent structural limitations when analyzed from a systemic risk anticipation perspective. Specifically, these systems are designed to detect fatigue manifestations rather than anticipate pre-failure systemic states associated with high-potential energy transfer. OAS outputs are primarily interpreted as discrete events, triggering localized responses such as task reassignments, rest breaks, or temporary equipment shutdowns. While these actions are necessary and effective at the operational level, they do not address the latent accumulation of risk factors that may be developing throughout shifts, production cycles, or operational interfaces. Furthermore, OAS systems do not inherently integrate contextual variables such as operational pace, exposure density, task criticality, or accumulated system stress. Consequently, they provide limited visibility into whether the operational system is approaching a critical threshold where a fatigue-induced loss of control could lead to a high-severity event. This structural limitation highlights a significant gap between fatigue detection and fatigue risk anticipation, reinforcing the need for a complementary framework capable of transforming existing OAS data into an anticipatory representation of risk at a systemic level. 5.2 QPI redesign using OAS as the backbone (integrated model) 5.2.1 New Potential Systemic State S-FAT-OAS Status High-energy event potential arising from fatigue/drowsiness in haulage, before the critical alarm occurs.
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