Aegir Predictive Maintenance Solution

Human Insight + Model-Driven Support = Better Predictions

If you manage critical equipment, you know unexpected failures can cost millions. Power providers now have more sensor data than ever, but interpreting it before issues escalate remains a challenge.

Aegir Predictive Maintenance Solution™ takes the guesswork out of predictive maintenance with:

  • Actionable insights from complex sensor streams and maintenance records
  • Early detection of anomalies, empowering engineers to address potential risks
  • Proactive alerts to prevent costly unplanned downtime

Whether you’re managing legacy systems or planning for growth, Aegir ensures your assets are protected, your operations are optimized, and your teams are ready.

Ready to See Aegir in Action?

Innovative Features

Aegir blends human and artificial intelligence to deliver results you can act on.
Explore Dynamic Equipment Conditions

Build Analytics on Historical Records

Integrate with Any Manufacturer

Aegir turns complex sensor data into clear insights.

Diagram of hydroelectric generator with sensors

How Does Aegir Work?

  • Aegir ingests sensor data and maintenance records, integrating with AVEVA, IBM Maximo, Cognite, and others to power AI models with real-time insights on equipment health and performance.
  • Aegir’s event processor, powered by Elder Research’s CEPtor, filters high-frequency sensor data to highlight only key events, reducing noise and improving model accuracy.

    Aegir data collation
  • The Aegir platform is developed utilizing cloud services by default yet portable to premise as needed. The cloud infrastructure allows for “as needed” scaling without a separate engineering exercise.

    Our data scientists are not bogged down in software or infrastructure setup when developing new models, as the model development environment allows them to focus on the best-fit models for the targeted pursuit.

    This same environment can be offered to customers for internal development.
  • This is a snapshot of some of the models employed as part of Aegir, but the list continues to grow.

    Predictive Models
    • Survival Models: predict major faults of generator and turbine within 12 weeks
    • Deep Learning Models: predict major faults of generator and turbine within 4 weeks
    • Trained across all units, then refined for a given unit
    Anomaly Models
    • Startup timing: identify unusual startup sequence behavior
    • Subsequence model: identify unusual sensor activity in a subsequence
    • Shutdown timing: identify unusual shutdown sequence behavior
  • Aegir provides a series of visuals organized around cases and evidence to support a prediction deemed worthy of investigation. The visuals support the model scores and allow for establishing context around the event(s) that support the case using the data collected (sensor or maintenance records). Each case and its evidence can then be labeled by operations personnel to further inform the models.

    Aegir system overview

    Aegir condition propagator grade over time

    Aegir condition propagator damage exposure detail

Expert Advisors

To keep Aegir on the cutting edge, Elder Research consults with subject matter experts who advise on new developments.

Aegir advisor John Yale

John Yale

John Yale is an expert in electrical equipment for hydroelectric generation, specializing in generators, excitation, and control systems. He has over 30 years of experience in design, construction, commissioning, and operations. A Certified Asset Manager through the Institute of Asset Management, John also developed an asset management program for a major public utility, which included a comprehensive inspection and condition assessment. He is an active member of CEATI, serving as chair of the hydroAMP Steering Committee and as a founding member of the Asset Management Task Force.

Aegir advisor Uros Stevanovic

Uros Stevanovic

A senior domain expert, Uros Stevanovic thrives on solving interdisciplinary engineering problems. He has spent over 15 years working in operation and maintenance of power system assets and project execution of critical infrastructure. As an asset owner and subject matter expert, Uros successfully guided his company’s strategic transition to predictive maintenance—bolstered by advanced analytics. Uros holds a Master of Science in Electrical Engineering and Computer Science from Belgrade University.

Want to learn more?

Download the guide below or read the case study.

 

In this guide you will learn...

1. How you can incorporate different data sources into a predictive approach.

2. How to conceptualize risk of individual components and tie it to the entire system.

3. How Elder Research implemented the system into production for a European hydropower operator, quickly driving 45% ROI in the first year alone.

4. About Elder Research’s proprietary finite state machine tool (CEPtor) and visualization tool (RADR).

See Aegir's Impact

Boosting predictive performance by 500%? Achieving 45% ROI in just a the first year? See how Aegir can impact you.
Show me Aegir's impact