The model is based on two fundamental conceptual principles: 4.1.Overlap of states Before an event materializes, the operating system can simultaneously exist in multiple potential configurations. Each of these configurations possesses a distinct level of operational energy, systemic tension, and materialization potential, even though not all are directly observable through traditional indicators. ( 1, 2,…, ) From this perspective, fatigue is not manifested solely as a single event, but as a result of the dynamic interaction of multiple variables that coexist in the system before its explicit manifestation. 4.2.System collapses due to decision or condition When the operation selects a specific course of action, or when a critical condition occurs (for example, a high-severity fatigue alert), the system collapses into a single observable state. At this point, the event ceases to be potential and becomes part of operational history. The QPIFAT is geared towards observing and quantifying the system before this collapse, when it is still possible to intervene in a preventive and less disruptive way. The QPIFAT proposes to represent potential states using: ● Observable precursor variables (O): frequency and severity of fatigue alerts, recurrence rates, operational response times. ● Latent variables not directly measurable (L): Trends in accumulated fatigue, operational pressure, actual effectiveness of recovery actions. ● Dynamic human factors (H): level of experience, overtime, rotation patterns and shift adaptation. ● Environmental and energy conditions (E): night operation, visibility, presence of water, track geometry and physical-environmental condition of the cabin (vibration, temperature, dust and noise). 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. 6
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