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Transport & Logistics — Asset Health Monitoring for Predictive Maintenance

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This DAG continuously monitors asset health using IoT sensors and SCADA systems to enable proactive maintenance. It leverages machine learning for anomaly detection, enhancing operational efficiency and reducing downtime.

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Overview

The purpose of the 'Asset Health Monitoring for Predictive Maintenance' DAG is to ensure the reliability and efficiency of transport and logistics assets through continuous health monitoring. This DAG collects real-time data from IoT sensors and SCADA systems, providing a comprehensive view of asset performance. The ingestion pipeline normalizes and timestamps the incoming data, ensuring consistency for subsequent processing. Machine learning models are applied to detect anomalies, allowing for

The purpose of the 'Asset Health Monitoring for Predictive Maintenance' DAG is to ensure the reliability and efficiency of transport and logistics assets through continuous health monitoring. This DAG collects real-time data from IoT sensors and SCADA systems, providing a comprehensive view of asset performance. The ingestion pipeline normalizes and timestamps the incoming data, ensuring consistency for subsequent processing. Machine learning models are applied to detect anomalies, allowing for early identification of potential failures. Alerts are generated and sent to maintenance teams via an IT Service Management (ITSM) system, facilitating timely interventions. Key performance indicators (KPIs) such as Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR) are monitored to evaluate asset reliability and maintenance effectiveness. Additionally, failure recovery procedures are established to minimize operational disruptions. The outputs of this DAG include detailed health reports, anomaly alerts, and performance metrics, which are crucial for informed decision-making. By implementing this predictive maintenance solution, organizations can significantly reduce unplanned downtimes, optimize maintenance schedules, and ultimately enhance asset longevity and performance, delivering substantial business value in the transport and logistics sector.

Part of the Predictive Maintenance solution for the Transport & Logistics industry.

Use cases

  • Reduced unplanned downtime through proactive maintenance
  • Improved asset reliability and performance metrics
  • Optimized maintenance schedules based on predictive insights
  • Enhanced decision-making with real-time health reports
  • Increased operational efficiency in transport and logistics

Technical Specifications

Inputs

  • • Real-time IoT sensor data streams
  • • SCADA system performance metrics
  • • Historical maintenance records
  • • Environmental condition data
  • • Operational workflow logs

Outputs

  • • Anomaly detection alerts for maintenance teams
  • • Detailed asset health reports
  • • Performance metrics dashboards
  • • Maintenance scheduling recommendations
  • • Failure recovery procedure documentation

Processing Steps

  1. 1. Collect real-time data from IoT sensors
  2. 2. Normalize and timestamp incoming data
  3. 3. Apply machine learning models for anomaly detection
  4. 4. Generate alerts for detected anomalies
  5. 5. Monitor and calculate MTBF and MTTR
  6. 6. Compile health reports and performance metrics
  7. 7. Send alerts and reports to maintenance teams

Additional Information

DAG ID

WK-1273

Last Updated

2025-09-09

Downloads

27

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