Multi-Modal AI for Predictive Network Maintenance Using Telemetry and Images
Dr. Jie Xu, Dr. Olav Tirkkonen
Aalto University / Nokia Bell Labs
Abstract
We propose a multi-modal AI system for predictive maintenance that fuses network telemetry data (KPIs, alarms, counters) with visual inspection data (drone images, thermal camera feeds) from cell sites. A cross-modal attention mechanism learns correlations between equipment visual condition and network performance degradation patterns. The system predicts equipment failures 14 days in advance with 92% precision, reducing emergency site visits by 55% in a 6-month field trial with a European operator.
AI Summary
- Multi-modal AI fusing network telemetry with drone/thermal imagery for maintenance.
- Predicts equipment failures 14 days in advance with 92% precision.
- 55% reduction in emergency site visits during 6-month field trial.
- Cross-modal attention learns visual-telemetry correlations.
Key Findings
- 1Visual data reveals corrosion and physical damage invisible to telemetry alone.
- 2Combined modalities catch 25% more failures than either modality alone.
- 3Thermal anomalies in power amplifiers precede performance degradation by 2-3 weeks.
Industry Implications
Transforms maintenance from reactive to truly predictive.
Reduces operational costs through fewer emergency dispatches.
Demonstrates the value of multi-modal AI fusion for network operations.
Read the Original Paper
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