Level-1 Digital Twins
Level 1 digital twins are discussed and implied liberally in literature and elsewhere. However, such digital twins do not exist for reasonably complex machines and systems.
Despite the significant efforts of engineers and scientists at national laboratories and universities, such digital twins have been substantially barred from being realized due to physical and mathematical realities that have prohibited the liberal sensing of many internal hidden time-varying attributes of a complex system. Without such sensing capabilities, "twins" are just remote displays, with scarce capability to provide insight into novel behaviors.
Our inferential sensing technology, however, has overcome such barriers. ​It utilizes the readily available inputs and outputs of a system or environment to perform accurate real-time inferential sensing of 100's of hidden physical attributes and stochastic behaviors that are otherwise impractical or impossible to measure.
While AI-based, it is trained using only reduced-order simulation data. Because it does not require new hardware installed (e.g., advanced sensors), nor extensive failure histories performed on your high-value systems, our AI technology enables comprehensive precision predictive maintenance and fault-tolerant control at likely a 1000th the current market cost at 100x the performance related impact to your ROI.
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Products will be available in mid 2026. The technology is ready now. Pass our information along to your theoretical people. Early collaborations are encouraged.
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Advanced AI-Based Predictive Maintenance
Track and quantify system performance degradations and predict component maintenance rquirements for high-value machines and complex processes, without adding additional sensors.
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Reduce shutdowns and maintenance operations costs​
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Improve product yield
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Increase safety
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Maintain compliance
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Reduce emissions
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Increase revenue
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Comprehensive Inferential Sensing
Our technology provides control systems comprehensive coverage of a physical attributes, disturbances, and configurations concerning time-varying systems and environments.
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Applications for expanded capabilities include:
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Complex Dynamical Threat Environment Situational Awareness for Satellites and Submarines
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Failure Avoidance and Fault-Tolerance Capabilities in Aircraft and Spacecraft
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Auto-Tuning Control Systems Concerning Manufacturing Tolerances and System Maintenance
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Life-Cycle Management of Distributed Machines and Robots
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Robot Swarm Management and Autonomous Planning and Reconfiguration
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High Fidelity Performance Digital Twinning
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Autonomous Process Control
Relative to the State of The Art
There are many inferential sensing methods in practice, including frequent innovations in the literature.
However, all such methods are mathematically limited by a maximum number of parameter estimates (not including state estimates) that is roughly equal to the number of independent output signals used to derive the estimates. Such parameters are physical attributes and disturbances, including system degradations, engineering deviations, and environmental disturbances.
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Our technology overcomes traditional limits to the number of time-varying parameters that can be estimated in real-time to accurately infer essentially any number of time-varying parameters from scarce output signal resources, e.g., potentially providing one hundred or more distinct parameter estimates based on the monitoring of a single system output.
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