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Copula-Based Uncertainty Quantification (Copula-UQ) for Multi-Sensor Data in Structural Health Monitoring
He-Qing Mu1,2,3, Han-Teng Liu1, Ji-Hui Shen1
1School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China.
Sensors (Basel, Switzerland)
|October 10, 2020
Summary
This study introduces Copula-UQ for uncertainty quantification in structural health monitoring using multi-sensor data. It enables accurate joint probability density function modeling and prediction, even with incomplete sensor information.
Area of Science:
- Engineering
- Statistics
- Data Science
Background:
- Uncertainty quantification (UQ) is crucial for multi-sensor data in structural health monitoring (SHM).
- Multivariate joint probability density function (PDF) modeling is a key challenge in SHM.
- Copula-based methods offer a way to decouple marginal PDF inference from dependence structure analysis.
Purpose of the Study:
- To propose Copula-UQ, a framework for UQ in SHM using multi-sensor data.
- To integrate multivariate joint PDF modeling, model class selection, parameter identification, and probabilistic prediction.
- To handle incomplete information arising from sensor faults.
Main Methods:
- Modeling multivariate joint PDFs using univariate marginal PDFs and copulas.
- Employing a combination of inference functions for margins and maximum likelihood estimation.
- Performing probabilistic predictions of target variables using complete or incomplete predictor data.
Main Results:
- Demonstrated capability in joint PDF modeling for multi-sensor data.
- Successfully predicted target variables using both complete and incomplete predictor information.
- Validated the Copula-UQ framework with simulated and real-world bridge temperature data.
Conclusions:
- Copula-UQ effectively addresses UQ challenges in SHM for multi-sensor systems.
- The method provides robust joint PDF modeling and accurate probabilistic predictions.
- The framework is capable of handling missing data due to sensor issues.
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