5 papers
Gradient-enhanced spline dimensional decomposition for uncertainty quantification with limited training samples
Eunho Heo, Dongjin Lee
A spline dimensional decomposition (SDD) surrogate effectively represents high-dimensional engineering responses with localized features and complex nonlinearities in uncertainty q…
Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders
Youngjae Jeon, Dongjin Lee
Reliable fault diagnosis of rotating machinery is essential for the safe and stable operation of industrial systems. Although deep learning methods perform well under closed-set co…
Data-driven dimensionally decomposed generalized polynomial chaos expansion for forward uncertainty quantification
Hojun Choi, Eunho Heo, Dongjin Lee
Dimensionally decomposed generalized polynomial chaos expansion (DD-GPCE) efficiently performs forward uncertainty quantification (UQ) in complex engineering systems with high-dime…
A Robust Method for Fault Detection and Severity Estimation in Mechanical Vibration Data
Youngjae Jeon, Eunho Heo, Jinmo Lee +2
This paper proposes a robust method for fault detection and severity estimation in multivariate time-series data to enhance predictive maintenance of mechanical systems. We use the…
An approximate control variates approach to multifidelity distribution estimation
Ruijian Han, Boris Kramer, Dongjin Lee +2
Forward simulation-based uncertainty quantification that studies the distribution of quantities of interest (QoI) is a crucial component for computationally robust engineering desi…