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Artificial Intelligence

Predictive Maintenance for Improved Router Performance

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Overview

Poor router performance can lead to disruptions and dissatisfaction for users. To tackle this, Digica partnered with a company to predict router performance issues and prevent them using machine learning methods.

 

Objectives

  • Indicate reboot probability using an XGBoost algorithm.
  • Identify detailed reasons for reboots and suggest preventative steps.
  • Cluster devices to train specialized models for more accurate results.
Predictive Maintenance for Improved Router Performance
 

Results

  • Created an ML model achieving >80% precision and >85% accuracy.
  • Predicted reboots 12 hours in advance, giving operators time to take preventive action.
  • The model was deployed in production, with big data tools used to handle 10M+ devices per hour.
  • Real-time predictions and insights were made available via Tableau dashboards.
  • Cloud infrastructure was critical in handling the large-scale data involved:
    • One day of raw data: ~100GB
    • Total dataset size: 16 TB
    • Number of features: >500 (Data was collected from each device every few minutes)

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