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Defense & Aerospace — Health Feature Calculation for Predictive Maintenance

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This DAG calculates health features for predictive maintenance by leveraging machine learning models to estimate Remaining Useful Life (RUL) of equipment. It integrates processed data into a Computerized Maintenance Management System (CMMS), enhancing maintenance decision-making.

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Overview

The purpose of this DAG is to derive health features from normalized data, which are crucial for predictive maintenance in the Defense and Aerospace industry. It begins by ingesting data from various sources, including sensor readings, maintenance logs, and operational performance metrics. The data is then processed through a series of machine learning algorithms designed to predict the Remaining Useful Life (RUL) of equipment. These algorithms analyze historical performance and failure data to

The purpose of this DAG is to derive health features from normalized data, which are crucial for predictive maintenance in the Defense and Aerospace industry. It begins by ingesting data from various sources, including sensor readings, maintenance logs, and operational performance metrics. The data is then processed through a series of machine learning algorithms designed to predict the Remaining Useful Life (RUL) of equipment. These algorithms analyze historical performance and failure data to extract relevant health features, which are essential for anticipating maintenance needs and minimizing downtime. Quality control measures are implemented to ensure the accuracy of predictions, with monitoring of key performance indicators (KPIs) such as prediction accuracy and computation time. In the event of a failure during processing, the DAG is designed to restart from the last successful step, ensuring reliability and robustness. The final output is integrated into a Computerized Maintenance Management System (CMMS), providing maintenance teams with actionable insights. The business value lies in improved maintenance scheduling, reduced equipment failures, and enhanced operational efficiency, ultimately leading to cost savings and increased mission readiness.

Part of the Predictive Maintenance solution for the Defense & Aerospace industry.

Use cases

  • Enhances equipment reliability and operational readiness
  • Reduces unexpected maintenance costs and downtime
  • Improves decision-making with data-driven insights
  • Increases lifespan of critical defense assets
  • Facilitates compliance with regulatory maintenance standards

Technical Specifications

Inputs

  • • Sensor data from aircraft and equipment
  • • Historical maintenance logs
  • • Operational performance metrics
  • • Failure event records
  • • Normalized health data

Outputs

  • • Predicted Remaining Useful Life (RUL) reports
  • • Health feature datasets for analysis
  • • Alerts for maintenance scheduling
  • • Integration reports for CMMS
  • • Performance KPI dashboards

Processing Steps

  1. 1. Ingest data from multiple sources
  2. 2. Normalize and preprocess data
  3. 3. Apply machine learning models for RUL prediction
  4. 4. Extract health features from processed data
  5. 5. Evaluate prediction accuracy and quality controls
  6. 6. Generate outputs for CMMS integration
  7. 7. Monitor KPIs and log processing metrics

Additional Information

DAG ID

WK-0731

Last Updated

2026-02-02

Downloads

32

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