ROC Situational Awareness Dashboard Design Principles
A ROC Situational Awareness Dashboard is like a mission control center’s 'live dashboard' — it shows real-time, reliable, and actionable information about all remote mine sites in one place so operators can see what’s happening, spot problems early, and act decisively.
⚠️ Why It Matters
📘 Definition
The ROC Situational Awareness Dashboard is a human-centered, fault-tolerant software system integrating telemetry, alarm management, geospatial context, and workflow-aware visualization to support cognitive task performance by remote operators managing distributed mining assets. It conforms to ISO 9241-210 (human-centred design) and IEC 62591 (WirelessHART) interoperability principles, with architecture aligned to NIST SP 800-82 for industrial control system security. Its core function is to reduce situation awareness degradation across time, space, and operational abstraction layers.
🎨 Concept Diagram
AI-generated illustration for visual understanding
💡 Engineering Insight
Dashboards don’t fail because of missing features — they fail because they violate the operator’s mental model of the system. The most effective ROC dashboards are built backward: start with the operator’s decision tree for a Tier-1 failure (e.g., ‘mill trip’), then instrument only the data needed to answer each branching question — nothing more, nothing less. Every extra widget erodes SA faster than missing data.
📖 Detailed Explanation
Advanced implementation recognizes that SA is not static but *dynamic and hierarchical*. Operators shift attention between strategic (e.g., fleet utilization across 4 sites), tactical (e.g., current crusher circuit status), and micro-tactical (e.g., bearing temperature trend on Pump-7B) layers — and the dashboard must support seamless, low-friction transitions. This requires layered visualization (e.g., top-level heatmaps → drill-down to time-series + event correlation graphs) and adaptive information density.
The highest maturity dashboards embed *predictive SA*: they don’t just show current state — they project likely future states using lightweight physics-informed models (e.g., ore flow continuity prediction from bin level + feeder speed + moisture sensor fusion). These projections are rendered with calibrated uncertainty bounds (per ISO/IEC 14763-3), enabling operators to distinguish between probable outcomes and outliers requiring intervention — turning passive monitoring into anticipatory control.
🔄 Engineering Workflow
📋 Decision Guide
| Rock/Field Condition | Recommended Design Action |
|---|---|
| Multi-site alarm flood (>0.3 alarms/100 tags/min) + latency >12 sec | Activate ‘SA Preservation Mode’: suppress non-critical alarms, overlay root-cause path, lock map view to affected asset cluster |
| Geospatial context loss (e.g., GPS drift >15 m on haul truck fleet) | Switch to fused INS/GNSS mode; render uncertainty ellipses; disable auto-zoom; flag affected telemetry with amber border |
| Critical process deviation (e.g., crusher throughput <70% nominal for >90 sec) + no alarm raised | Trigger silent diagnostic overlay: show upstream/downstream KPIs, recent maintenance logs, and last calibration timestamp |
📊 Key Properties & Parameters
Data Latency
2–15 seconds (telemetry), ≤60 seconds (batch analytics)Time delay between physical event occurrence at site and its representation on the dashboard
Latency >8 sec degrades operator response time to critical alarms by ≥37% (per NASA HRP-2022 studies)
Alarm Density
0.05–0.3 alarms/100 tags/minNumber of active high-priority alarms per 100 monitored tags per minute
Alarm density >0.25/100 tags/min increases operator cognitive load beyond validated SA thresholds (ISA-18.2 Annex B)
Context Fidelity
65–92% (measured via operator recall accuracy in controlled SA trials)Degree to which dashboard visualizations preserve spatial, temporal, and causal relationships among assets and events
Fidelity <75% correlates with 4.3× higher misdiagnosis rate of cascading failures (Rio Tinto ROC Validation Report, 2023)
Workflow Alignment Index (WAI)
0.68–0.91Normalized score (0–1) quantifying how closely dashboard layout matches actual operator task sequence per ISA-101.01
WAI <0.75 increases average task completion time by 22–38% and error rate by 2.1× (BHP Pilbara ROC Usability Benchmark)
📐 Key Formulas
Situation Awareness Index (SAI)
SAI = (Perception Score × 0.3) + (Comprehension Score × 0.4) + (Projection Score × 0.3)Composite metric derived from validated SA probe (SAGAT) scoring across three Endsley levels
| Symbol | Name | Unit | Description |
|---|---|---|---|
| Perception Score | Perception Score | Score reflecting accuracy of environmental element detection (Endsley Level 1) | |
| Comprehension Score | Comprehension Score | Score reflecting understanding of current situation and relationships among elements (Endsley Level 2) | |
| Projection Score | Projection Score | Score reflecting ability to anticipate future states (Endsley Level 3) |
Alarm Load Ratio (ALR)
ALR = (Active High-Priority Alarms / Total Monitored Tags) × 100Quantifies cognitive pressure from concurrent alarms per tag population
| Symbol | Name | Unit | Description |
|---|---|---|---|
| Active High-Priority Alarms | Active High-Priority Alarms | count | Number of high-priority alarms currently active |
| Total Monitored Tags | Total Monitored Tags | count | Total number of process tags under alarm monitoring |
🏭 Engineering Example
Roy Hill Iron Ore – Central ROC (Pilbara, WA)
Banded Iron Formation (BIF)🏗️ Applications
- Real-time fleet dispatch coordination across 5+ open-pit mines
- Predictive shutdown sequencing during power grid instability
- Cross-site water balance optimization during cyclone season
🔧 Try It: Interactive Calculator
📋 Real Project Case
Iron Ore Mine ROC Consolidation in Western Australia
Rio Tinto’s Pilbara ROC consolidation across 8 open pit sites