Case Studies

Predictive Maintenance in Action

we provide detailed insights into how ENERGY7’s predictive maintenance solutions have been deployed across multiple railway divisions. Our system generates real-time alerts for all stations in a division, focusing on two key categories.

  • Threshold Voilation
  • AI Model Prediction

Threshold Violations

Our IoT sensors continuously monitor critical railway assets like tracks, point machines, signals, and more. Whenever these assets exceed predefined thresholds (e.g., voltage, current, temperature), the system triggers alerts. These threshold violations are reported in division-specific PDFs, giving operators instant visibility into potential issues across all stations.

AI Model Predictions

Using advanced AI models, we predict potential failures by analyzing patterns in real-time data. The alerts generated under these AI models help identify components that are showing early signs of failure, enabling timely intervention. In the division PDF, we provide details of stations at risk, including predictions about the chances of failure over the coming weeks based on historical and real-time data.

Enhancing Safety and Efficiency

  • Our predictive alerts go beyond simple threshold violations. Our system integrates AI-based algorithms to detect more subtle indicators of potential problems, such as:

    • Early-stage wear and tear on tracks and point machines.
    • Signal system anomalies that may not yet trigger alarms but indicate future problems.
    • Vibration analysis to detect alignment or mechanical issues in point machines.
    • Power supply inconsistencies that could lead to failure of key operational system

Visualizing Performance

we provide detailed performance graphs, particularly focusing on point machines. These graphs, included in our division-specific PDFs, illustrate the performance trends of point machines

Avg & peak current draw

To assess the mechanical load and overall health of the point machines.

Case Studies

Predictive Maintenance in Action

we provide detailed insights into how ENERGY7’s predictive maintenance solutions have been deployed across multiple railway divisions. Our system generates real-time alerts for all stations in a division, focusing on two key categories.

  • Threshold Voilation
  • AI Model Prediction

Threshold Violations

Our IoT sensors continuously monitor critical railway assets like tracks, point machines, signals, and more. Whenever these assets exceed predefined thresholds (e.g., voltage, current, temperature), the system triggers alerts. These threshold violations are reported in division-specific PDFs, giving operators instant visibility into potential issues across all stations.

AI Model Predictions

Using advanced AI models, we predict potential failures by analyzing patterns in real-time data. The alerts generated under these AI models help identify components that are showing early signs of failure, enabling timely intervention. In the division PDF, we provide details of stations at risk, including predictions about the chances of failure over the coming weeks based on historical and real-time data.

Enhancing Safety and Efficiency

  • Our predictive alerts go beyond simple threshold violations. Our system integrates AI-based algorithms to detect more subtle indicators of potential problems, such as:

    • Early-stage wear and tear on tracks and point machines.
    • Signal system anomalies that may not yet trigger alarms but indicate future problems.
    • Vibration analysis to detect alignment or mechanical issues in point machines.
    • Power supply inconsistencies that could lead to failure of key operational system

Visualizing Performance

we provide detailed performance graphs, particularly focusing on point machines. These graphs, included in our division-specific PDFs, illustrate the performance trends of point machines

Avg & peak current draw

To assess the mechanical load and overall health of the point machines.