Predictive maintenance
Never let equipment deteriorate. Make sure you know the state of your assets through real-time network monitoring, to conduct predictive maintenance.
Traditional maintenance strategies, based on fixed schedules, often lead to inefficiencies. Without a proactive approach, critical assets can deteriorate, leading to costly repairs and operational disruptions.
Thanks to AI-powered network monitoring, you can detect potential failures before they become major issues and run predictive maintenance operations. By continuously assessing equipment health, organizations can ensure optimal performance, reduce downtime, and minimize repair costs.
AI-Powered Network Monitoring for Proactive Infrastructure Management
With Deepomatic, infrastructure owners gain real-time, data-driven insights into asset conditions, transforming every operation into valuable data including photographic records and action histories. You can conduct predictive maintenance, detecting wear patterns early to prevent costly failures and extend asset lifespan.
The full traceability ensures accountability, helping assign responsibility for equipment degradation and optimize restoration costs effortlessly.
Improved equipment availability
Predictive maintenance ensures critical infrastructure remains operational by identifying potential failures in advance. This proactive approach minimizes unexpected breakdowns, keeping business operations uninterrupted.
Controlled maintenance costs
By eliminating unnecessary site visits and reducing the need for full-scale equipment overhauls, predictive maintenance optimizes maintenance budgets and maximizes resource allocation.
Long-term infrastructure value protection
Addressing maintenance needs before they become critical safeguards infrastructure integrity. Predictive maintenance helps preserve the long-term value of assets, ensuring durability and efficiency over time.
AI-Driven Asset Health Scores for Smarter Decision-Making
Deepomatic’s Asset Management function uses AI-powered analysis of equipment images to assign a real-time health score. Parameters influencing the score include: presence of waste, wear and tear traces, collapsing parts, broken parts, etc.) This score evolves as operations progress, providing a clear visual representation of asset conditions over time.
- Take Preventive Action Before Critical Failures Occur
- Track the photo history of deteriorating equipment to identify responsible parties
How does Deepomatic’s AI platform contribute to Predictive Maintenance?
Systematic quality control to monitor the infrastructure
Every time a field worker works on an infrastructure asset and takes photos of their work, the AI analyses the quality of their job, but also the physical state of the asset. Each operation therefore becomes an inspection which assesses the level of potential degradation of equipment.
Health score generation
Deepomatic developed an Asset Management feature which calculates a health score based on the level of degradation detected during each operation on the asset.
Scheduling of maintenance operations based on health score
Infrastructure managers can leverage the health score to plan maintenance operations on assets that require quick repairing, before they completely break down. This is what TDF, the French infrastructure operator, is doing on its fiber street cabinets.
Benefits of using AI for Predictive Maintenance
Minimized asset downtime
AI enables operators to plan efficient repair operations based on asset condition rather than reacting blindly to unexpected breakdowns. This proactive approach prevents severe damage that could necessitate costly repairs or complete asset replacement, saving thousands of dollars and reducing the need for prolonged technician involvement.
Extended asset lifespan
By leveraging AI for monitoring and implementing predictive maintenance, assets, such as fiber infrastructure, are preserved for a longer duration. This ensures a higher return on investment for operators and enhances the profitability of their infrastructure.
Optimization of maintenance costs
AI allows infrastructure companies to prioritize maintenance tasks based on real-time needs rather than adhering to rigid schedules. This targeted approach increases efficiency, improves resource allocation, and significantly reduces operational expenses.