Where are the Level 3+ Digital Twins?
Level 3+ digital twins are discussed liberally in literature and elsewhere. However, beyond academic interests, level 3 digital twins do not exist for complex machines and systems.
Despite the significant efforts of engineers and scientists at national laboratories and universities, level 3 digital twins have been substantially barred from being realized for complex systems due to physical and mathematical realities that have prohibited a sufficient inferential 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 stochastic and novel behaviors and disturbances.
Our recently developed 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.
Further, our AI technology it is trained using only reduced-order simulation data. Therefore, because it does not require new sensor hardware installed to enable 100x the sensitivity nor does it required extensive failure histories from your high-value systems to learn system ill-behaviors, our AI technology can perform superior comprehensive inferential sensing and situational awareness at about a1000th the market-typical start-up and implementation costs.
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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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