Keeping Networks Humming with External Data – An Enhanced AI Driven Predictive Maintenance Approach
Network outages are the bane of service providers and operators. When networks go down, customers are impacted, revenue is lost, and reputations suffer. According to Gartner and various studies, the average cost of network downtime can range from $100,000 to over $1 million per hour.
Legacy networks were reactive in their maintenance approach. Engineers scrambled when problems occurred, leading to prolonged outages and dissatisfied users. But in today’s digital era, customers demand always-on connectivity and zero tolerance for downtime. How can operators better predict network issues and prevent them from occurring?
This is where machine learning (ML) driven predictive maintenance comes in. By leveraging large volumes of historical network data, ML models can forecast potential network faults and degrade situations before they even happen. The key is identifying patterns and correlations that provide advanced warning.
Typical predictive maintenance solutions analyze performance metrics like network alarms, trouble tickets, and KPIs. However, external factors like extreme temperature and weather can also significantly impact network health. Both high and low temperatures can degrade and cause failure of components like optical equipment, radios, cables and more. By incorporating additional data on equipment/component temperature and local weather conditions, operators can boost the accuracy of ML predictions.
Here’s how it works:
- Historical network fault data is fed into ML models along with temperature and weather data from the time of each event.
- The algorithms analyze massive combinations of network, equipment, temperature and weather metrics to find high-probability correlations.
- Models show the probability of network element failures under high or low-temperature conditions.
- Predictions are made on likely network faults up to 2-3 days in advance due to temperature extremes.
- Network engineers get insights to proactively prevent issues before they cascade.
- Maintenance can be scheduled with minimal downtime impact.
By combining weather and temperature data with historical network health metrics, the models provide a 360-degree view. Engineers get actionable heat maps, risk scores, and warnings.
Benefits of enhanced AI-driven predictive maintenance include:
- Up to 70% less network downtime from proactive prevention of failures.
- Estimate >50% reduction in ticket volume as issues are pre-emptively resolved.
- Tasks reduced by >50% within 2-3 months of implementation.
- Ability to identify root causes behind faults and implement remedy actions.
- Significant increase in network availability and performance.
- Increased cost savings by avoiding major repair costs.
- Maximized network utilization through ongoing optimization.
- Smoother user experience with fewer disruptions.
With climate change bringing more extreme heatwaves and cold snaps, networks face greater disruption risks from temperature extremes. AI predictive models incorporating temperature and weather data can forecast trouble spots so service providers and operators can harden infrastructure and dispatch crews as needed.
The bottom line? ML predictive analytics leveraging temperature and weather data provides a powerful tool for service providers and operators to keep their networks humming 24/7 and customers happily connected.
You can proceed to our website and download our paper on how we have implemented this in our product.
