Predictive Tower Maintenance
Cut truck rolls 30–50% by predicting equipment failures before they cause outages.
Urgency score — priority vs. other Telecom & Tower use cases
Overview
Inference models trained on equipment telemetry — power units, cooling systems, radios, backhaul links, generator run times — generate failure probability scores continuously. Field operations are dispatched proactively, before SLA-impacting failures occur, dramatically reducing both reactive callouts and unnecessary preventive visits.
The Penalty Stakes
- Generic fault databases built on industry-average data miss your equipment-specific failure signatures
- Environmental conditions (altitude, humidity, temperature range) vary significantly by market — require site-specific training
- Equipment age distribution and maintenance history are critical predictors — unavailable in any generic dataset
- Cloud-hosted maintenance AI creates dependency on external connectivity for a function that should operate during network stress
Business Impact
30–50% reduction in truck roll cost; fewer SLA breach penalties; lower insurance risk
Model accuracy depends on training on your specific equipment fleet and site conditions — generic fault databases underperform significantly
Infrastructure Requirements
Lightweight inference models deployed per site, processing sensor streams locally. Failure probability scores surfaced to field ops dashboards and CMMS systems. No raw sensor data leaves the site.
- NEXUS Foundry trains failure models on your specific equipment fleet, maintenance history, and environmental data
- NEXUS OS deploys lightweight inference across all sites without disrupting operations
- Failure probability scores push directly to your existing CMMS via API — no workflow change required
- On-site processing means no raw sensor data leaves the site — sensor telemetry stays on your infrastructure
- Models retrained on new maintenance outcomes — accuracy improves continuously as your fleet evolves