The Challenge

Rail systems face significant challenges in maintaining the health of wheels and tracks:

1. Risk of Derailments: Abnormal wear in wheels and tracks can lead to increased risk of derailments, posing serious safety hazards.
2. Maintenance Efficiency: Identifying the optimal frequency for maintenance activities to ensure safety while controlling costs.
3. Operational Downtime: Unplanned maintenance and repairs can lead to significant operational downtime and disruptions.

Key Features:

1. Real-Time Asset Locations:
   • An interactive map displays real-time GPS locations of rail assets, including trains, crossings, lines, maintenance vehicles, and substations.
2. Color-Coded Status Icons:
   • Assets on the map are marked with color-coded icons indicating their current operational status. Green signifies active and operational, yellow indicates warnings, and red represents critical issues needing immediate attention.
3. Overall Asset Status Summary:
   • A summary at the bottom provides a quick overview of the total number of active and inactive assets across various categories, including trains, crossings, lines, maintenance vehicles, and substations.
4. Recommendations Panel:
   • A list of active recommendations generated by the system’s rule logic, such as exceeded wheel wear thresholds, voltage fluctuations, and bogie health thresholds, ensuring immediate visibility of critical issues.
5. Equipment Requiring Maintenance:
   • A detailed graph and list show maintenance requirements across different assets, prioritizing those based on their upcoming service needs. This facilitates efficient maintenance scheduling and ensures timely interventions.
6. Drill-Down Capabilities:
   • Each section of the dashboard allows users to drill down into specific asset and recommendation details, providing granular insights and enabling targeted actions based on the system’s recommendations.
7. Alerts and Error Messages:
   • Real-time alerts and error messages are displayed, notifying maintenance teams of any detected anomalies that require immediate attention.
8. Interactive Map Controls:
   • Users can interact with the map to zoom in/out and click on specific assets to get more detailed information about their status and any ongoing issues.

This comprehensive view ensures that users have all the necessary information at their fingertips to monitor and manage rail assets efficiently, respond quickly to issues, and maintain optimal operational performance.

Key Features:

• Alerts Overview: Graphical representation of open alerts categorized by severity – no alerts, medium, and high severity.
• Work Order Status: Displays current status of each asset, categorized as available, in planning, or waiting.
• Performance Metrics (Last 30 Days): Summarizes key metrics such as new alerts, number of work orders, open work orders, and open work requests. It also tracks the duration from alert initiation to work order completion, comparing it with the previous 30-day period.
• Asset Filtering and Service Information: Enables filtering across all train assets, showing details like the last service date, upcoming service schedules, and due dates.
• Recent Recommendations: Lists recent recommendations triggered for train assets, with options for users to view details and take necessary actions.
• XMPro Co-Pilot Integration: Features an interactive block where users can query the AI model, trained on internal data like train engine manuals, for specific advice on errors, warnings, and issues. This information can be directly linked to work order requests and triage instructions.

This dashboard is designed for ease of use, allowing quick access to vital information and efficient management of train assets.

Key Features:

• Comprehensive Asset Metrics: In this detailed view for Bogie B001, we concentrate on specific metrics crucial for monitoring wheel wear.Key metrics include Spring Compression, indicating load distribution and wear patterns; Bearing Temperature, signaling potential friction issues; Bearing Vibration Amplitude, identifying internal wear; and Wheel Vibration Amplitude, detecting uneven wear or defects. These metrics collectively provide a crucial overview for maintaining wheel health and ensuring operational safety.
• Interactive 2D and 3D Asset Models: Features detailed 2D and 3D models of Train T001, with capabilities to ‘explode’ the view for in-depth examination of specific components. Notably, the 3D model visually identifies issues, such as those highlighted by recommendation rules related to wheel and track wear. For instance, areas of concern, like the wheels and bogie of affected wheels, are marked in red for easy identification.
• Error Identification and Actionable Recommendations: Clicking on highlighted errors directs users to the associated recommendations, where a range of actions can be explored to address the issue. This functionality is integrated with XMPro’s Recommendation Manager for efficient problem resolution.
• Detailed Train Information: The view encompasses comprehensive information about Train T001, including its type, model, description, and manufacturer details.
• XMPro Co-Pilot Integration: This feature includes XMPro Co-Pilot, trained on internal company data, providing users with specific guidance and answers to queries related to Train T001. This AI-driven support enhances decision-making and problem-solving related to the asset.

This Asset Analysis View is designed to provide users with a holistic understanding of Train T001, combining detailed visual models with actionable data and AI-assisted insights for effective asset management.

Embedded AI Agents

XMPro offers a variety of AI agents to support diverse operational needs, including:

• Azure OpenAI: Enhances natural language processing capabilities.
• OpenAI Assistant: Facilitates conversational AI integrations.
• Anomaly Detection: Identifies unusual patterns in data to prevent operational failures.
• Forecasting: Predicts future trends based on historical data.
• Kmeans Clustering: Groups similar data points for more effective analysis.
• MLflow: Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
• Regression: Provides predictive analytics to understand relationships between variables.

By embedding these powerful AI agents, XMPro transforms AI models into valuable assets that drive business growth and efficiency, bridging the gap between data flow and operational AI.