Environment-Specific Digital Twin for Asset Prognostic Surveillance
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Solution Overview
Problem
Existing digital twin-based prognostic-surveillance techniques struggle to effectively monitor engineering assets in outdoor environments due to variations in ambient temperature, humidity, and altitude, which cause performance issues not reflected in indoor digital twin simulations.
Innovation Solution
A system that performs environment-specific prognostic-surveillance by receiving time-series signals from assets, obtaining real-time environmental parameters, selecting an environment-specific inferential model trained on a golden system, generating estimated values, and performing pairwise-differencing operations to detect asset degradation, using techniques like SPRT and TPSS for real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a digital twin in a research facility is used for prognostic-surveillance, then performance comparison and degradation detection are enabled, but environmental variations in outdoor settings cause inaccurate monitoring
Solution Approach 1:
The patent applies parameter changes by adjusting the digital twin's environmental parameters (temperature, humidity, altitude) to match the actual outdoor operating conditions of the asset. This involves modifying the simulation parameters in the digital twin to reflect real-world environmental variations, thereby ensuring that performance comparisons and degradation detections remain accurate despite outdoor environmental fluctuations.
2Measurement precision
If environmental parameters are accounted for in outdoor monitoring, then accurate degradation detection is achieved, but system complexity increases
Solution Approach 1:
The patent introduces environmental parameters as intermediary variables that mediate between the asset's operational data and the digital twin's simulation model. By incorporating temperature, humidity, and altitude as intermediary factors, the system accurately accounts for environmental influences on asset performance without requiring fundamental changes to the core digital twin architecture, thus managing complexity while improving detection accuracy.
Data Source
AI summary
During operation, the system receives time-series signals from sensors in the asset while the asset is operating. Next, the system obtains real-time environmental parameters for an environment in which the asset is operating. The system then selects an environment-specific inferential model for the asset based on the real-time environmental parameters, wherein the environment-specific inferential model was trained on a golden system while the golden system was operating in an environment that matches the real-time environmental parameters. Next, the system uses the environment-specific inferential model to generate estimated values for the received time-series signals based on correlations among the received time-series signals, and performs a pairwise-differencing operation between actual values and the estimated values for the received time-series signals to produce residuals. Finally, the system determines from the residuals whether the asset is operating correctly.