Goal:
Automate manual tower inspections, improve safety, and create an AI-based visual inspection layer for proactive maintenance.
Impact Keywords:
- Predictive Maintenance
- Computer Vision
- Drone Automation
- Operational Safety
Approach:
LunarTech Lab deployed an end-to-end drone and vision pipeline integrated with asset-management systems.
- Autonomous Inspection Missions:
- Configured drones to follow pre-mapped flight paths and capture high-resolution imagery of towers and base stations.
- AI Vision Defect Detection:
- Trained custom CNN models to identify structural wear, corrosion, cable detachment, and antenna misalignment.
- Damage Risk Prediction:
- Combined defect severity with weather and vibration data to forecast failure probability within specific time windows.
- Maintenance Prioritization Engine:
- AI ranks inspection results by criticality and automatically generates maintenance work orders.
- Integration with Existing Systems:
- Linked output directly to telecom asset-management and ticketing platforms (ServiceNow, Maximo, or custom ERP).
Summary:
This case demonstrates how LunarTech Lab uses AI and automation to transform field operations — reducing manual inspections by 50%, improving safety, and predicting failures before service disruption occurs.
