Within the framework of Predictive Maintenance (PdM), Real-Time Asset Monitoring serves as the continuous “eyes and ears” of the network, providing the live data streams necessary to transition from reactive repairs to proactive upkeep. By integrating Internet of Things (IoT) sensors directly into the infrastructure, telecom operators can maintain constant oversight of their physical and virtual assets to ensure service continuity.
There are several critical functions and components of real-time monitoring within this context:
1. Continuous Oversight of Critical Infrastructure
Real-Time Asset Monitoring involves the continuous tracking of equipment performance across the entire network hierarchy. This includes:
- Network Hardware: AI-driven algorithms provide 24/7 monitoring of routers, switches, antennas, and base stations, analyzing performance data to identify early warning signs of failure.
- Data Centres: Monitoring extends to the facility level, where systems track server health, power consumption, and cooling systems to optimize resource allocation and prevent facility-wide outages.
- Edge and Fibre Systems: Real-time analytics are used to monitor signal quality and transmission data in fibre optic networks and cable systems, allowing for immediate intervention if connectivity degrades.
2. Immediate Alerting and Fault Detection
A primary goal of real-time monitoring is to enable instantaneous fault detection. By comparing live data against established baseline performance metrics, AI systems can:
- Detect Deviations: Machine learning models, such as random forests and neural networks, process live data in milliseconds to identify anomalies that deviate from the “normal” state.
- Swift Response: The systems provide real-time alerting, allowing technicians to respond to potential issues before they escalate into major service disruptions.
- Predictive Insights: Unlike traditional monitoring, which simply reports a current fault, this real-time data is used to forecast future failures with high accuracy.
3. Visualisation and Topology Mapping
Modern monitoring solutions go beyond simple data tables to provide comprehensive network visibility. For example, the startup MLNetworks offers a platform called SmartInsights that facilitates real-time end-to-end network visualisation. This allows operators to:
- Eliminate Silos: It supports multiple domains, including radio access networks (RAN), transport, and core networks, in a single view.
- Pinpoint Bottlenecks: Through topology mapping, operators can visualize every connection and “blueprint” of the network to find and resolve bottlenecks instantly.
4. Technical and Strategic Integration
To be effective, real-time monitoring must be deeply integrated with the operator’s broader digital strategy:
- Data Integration: Systems must manage vast amounts of heterogeneous data from diverse sources, ensuring that information from sensors is accessible in real-time for accurate modeling.
- Edge Computing: The integration of edge computing is a growing trend that reduces latency by processing data closer to the source, which is essential for time-sensitive maintenance decisions.
- Operational Efficiency: By automating the monitoring of routine tasks, AI reduces the need for manual inspections and lowers labour costs.
Analogy for Real-Time Asset Monitoring Imagine a telecom network is like a sophisticated modern aircraft on a long-distance flight. Traditional maintenance is like a mechanic inspecting the engines only after the plane has landed. Real-Time Asset Monitoring is like the array of sensors in the cockpit that provide the pilot with a live feed of every engine’s temperature, fuel flow, and vibration levels. If a single bolt begins to vibrate slightly out of rhythm, the pilot sees a warning light immediately, allowing them to adjust the flight path or landing before the passenger even notices a bump.
Craig Miles.
Founder & Director at Yesway Communications | Wireless Technology, Training & Two-Way Radio Solutions | Advancing Inclusive & Global Education Through Innovation