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Camera based Bottom View Monitoring & Anomaly Detection System

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Camera based Bottom View Monitoring & Anomaly Detection System



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Novius Railtech specializes in manufacturing advanced condition-based monitoring systems, including our Camera-Based Bottom View (Undercarriage) Monitoring & Anomaly Detection System. This system uses state-of-the-art machine vision technology to provide real-time, high-resolution inspections of critical undergear components such as wheels, axles, and brakes. It accurately detects anomalies, wear, and potential defects, allowing for early intervention and reducing costly repairs. With predictive maintenance capabilities, our system helps optimize operational efficiency, enhance safety, and extend the lifespan of railway assets. Novius Railtech’s Camera-Based Bottom View Monitoring System ensures reliable, cost-effective performance for modern rail systems.






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Features:


  • Real-Time, High-Resolution Monitoring: The Camera-Based Bottom View Monitoring System provides continuous, real-time inspection of the train’s undercarriage. Using advanced camera technology, it captures high-definition images to detect anomalies, wear, and potential defects on critical components like wheels, axles, and brakes.
  • Precise Anomaly Detection: With machine vision and intelligent image processing, the system identifies even the smallest faults or irregularities. This ensures early detection of issues, reducing the risk of major failures and enhancing overall system reliability.
  • Predictive Maintenance: By monitoring the condition of components in real-time and analyzing historical data, the system can predict when maintenance is needed. This allows operators to schedule repairs before failure occurs, reducing unexpected downtime and maintenance costs.
  • Cost Efficiency: This system reduces the need for manual inspections and unnecessary maintenance, focusing on targeted interventions. By identifying issues early, it helps minimize costly repairs and extends the lifespan of railway assets.




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