Military drones, or military UAVs, have become indispensable in modern warfare, transforming operational paradigms through reduced personnel risk and lower operational costs. However, safety hazards during their development remain significant. Current global standards lack systematic frameworks for safety risk management in military UAV development. This article proposes an optimized risk identification and assessment model based on empirical analysis of typical issues encountered during military drone projects.
Current Challenges in Military UAV Safety Risk Management
Flaws persist in hazard identification and risk evaluation for military drone programs. Key issues include:
- Incomplete Hazard Identification: Overemphasis on equipment while neglecting human factors and management deficiencies.
- Definitional Inconsistencies: Misalignment between hazard descriptions and associated risks (e.g., defining “personnel movement” as a hazard but labeling “unauthorized operations” as the risk).
- Coarse Process Segmentation: Grouping distinct high-risk operations (e.g., crane lifting and material transfer) into single units distorts risk prioritization.
Traditional risk assessment methods like the Graham Risk Assessment (LEC) exhibit critical limitations:
$$LEC = L \times E \times C$$
Where $L$=Likelihood, $E$=Exposure, $C$=Consequence. Subjectivity in scoring causes inconsistent risk ratings, with observed scores ranging from 6–63 for similar military UAV operations.
Optimization Framework
Our model integrates dual-prevention mechanisms and military UAV lifecycle specifics. Key enhancements include:
| Issue | Optimization Strategy |
|---|---|
| Vague hazard definitions | Standardize as: “Energy/hazardous material causing harm via unsafe conditions/behaviors/management defects” |
| Incomplete hazard coverage | Analyze three temporal states (past/present/future) and three operational states (normal/abnormal/emergency) |
| Poor risk categorization | Classify activities into: Design, Manufacturing, Testing, Office Management |
| Subjective risk scoring | Replace LEC with Risk Matrix (LS) method: $$Risk = Likelihood \times Severity$$ |
The LS matrix thresholds for military UAV development:
| Risk Level | Score Range | Response Protocol |
|---|---|---|
| Extreme (Red) | 15-25 | Immediate activity suspension |
| High (Orange) | 8-12 | Corrective actions within 24h |
| Medium (Yellow) | 4-6 | Mitigation within 72h |
| Low (Blue) | 1-3 | Routine monitoring |
Implementation Methodology
The optimized military UAV risk workflow comprises five phases:
- Preparation: Collect design schematics, material safety data sheets, and operational procedures
- Activity Segmentation: Divide processes using Job Hazard Analysis (JHA) into 81 sub-tasks across design, assembly, and testing
- Hazard Identification: Apply energy-based analysis across four dimensions: Human/Equipment/Environment/Management
- Risk Control: Implement hierarchy of controls:
| Priority | Measure Type | Military UAV Example |
|---|---|---|
| 1 | Engineering Controls | Torque specifications for critical fasteners |
| 2 | Administrative Controls | Rubber aging tests for landing gear |
| 3 | PPE | Anti-impact gear during engine testing |
Validation in a military drone project improved hazard detection by 37%, with design-phase risks constituting 48% of identified hazards versus 33% for administrative risks.
Conclusion
This model establishes a standardized framework for military UAV safety management, enhancing hazard visibility and control effectiveness. Continuous refinement remains essential as drone technologies evolve. Future work will integrate AI-driven predictive analytics for dynamic risk assessment in military drone development programs.