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Optimized Performance

Predict and prevent machine maintenance events. Ensure equipment uptime and material availability through the use of advanced data-driven and physics informed ML/AI. Our capability can be employed in the following frameworks:

  • Descriptive (Monitoring): Describes the machine condition based or real and/or virtual sensors. Answers the question: What happened? What is happening?
  • Diagnostics: Identifies the reason for a failure or degraded performance. Answers the question: Why did it happen?
  • Predictive (Prognostics): Predicts the machine’s future behavior. Answers the question: What is likely to happen?
  • Prescriptive: Prescribes how the machine should act based on the expected future behavior. Answers the question: How to Act in Response?

Create smart and autonomous machines by optimizing performance by using advanced ML/AI and optimal control frameworks. Enable:

  • System Performance Optimization: Optimize your operations and improve system level decisions making. For example, optimize ground vehicle fuel consumption performance based on mission specific parameters such as terrain, mileage, load, etc.
  • Autonomy & Robotics: Go from simple pre-programmed routines and execution driven by control-laws with pre-defined parameters to rapid autonomous planning and execution of maneuvers. Optimally balance autonomy objectives against sustainment objectives.

Case Studies

Remaining Useful Life
Thermomechanical Fatigue

Problem: Minimize equipment downtime related to hardware fatigue and failure.

Solution: Through the integration of governing equations into advanced machine learning algorithms, PredictiveIQ gives you full visibility into your asset’s remaining useful life.

Results: PredictiveIQ has reduced hardware product development and sustainment costs by 35%.

This modular, scalable architecture solution:

  • Allows a seamless addition of new failure modes.
  • Works with any telematics data stream.
  • Reduces by 1,000X the amount training data
  • Allows for real-time on Edge and on platform training.

Engine Health
Oil Quality & Wear Prediction

Problem: Reduce ground combat vehicles’ oil sample analysis frequency.

Solution: PredictiveIQ combines custom-made virtual sensing and vehicle telematics to provide real-time lube oil health status.

Results: PredictiveIQ has been proven to increase mission-focused equipment sustainment.

This modular, scalable architecture solution:

  • Allows a seamless addition of new failure modes.
  • Works with any telematics data stream.
  • Reduces by 1,000X the amount training data
  • Provides real-time monitoring of oil health while occasionally utilizing oil sample results for model calibration.

Vehicle Telematics
Anomaly Detection

Problem: Identify anomalies in engine telemetry data of a passenger vehicle.

Solution: PredictiveIQ developed a highly precise real-time PIML Classifier Software.

Results: Developed PIML model had a precision between 98.4% and 100% in detecting anomalies in incoming engine data.

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