Nonlinear Online Multioutput Gaussian Process for Multistream Data Informatics

Hu, Zhiyong; Wang, Chao

doi:10.1109/tii.2021.3111632

Cited by 9 publications

(1 citation statement)

References 21 publications

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“…Needless to say, this is a tedious task, especially for high complex models such as GPs. Recent studies propose online learning schemes for multi-output GPs to avoid model retraining from scratch [e.g., 25,26]. Nonetheless, their iterative and nonparallelizable nature greatly impedes their use when the number of online observations is excessive.…”

Section: Related Workmentioning

confidence: 99%

Federated Condition Monitoring Signal Prediction With Improved Generalization

Chung

Kontar

2024

IEEE Trans. Rel.

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Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer from a trade-off between representation power and agility in online settings. For instance, parametric methods that assume an underlying functional form for CM signals facilitate efficient online prediction updates. However, this simplification leads to vulnerability to model specifications and an inability to capture complex signals. On the other hand, approaches based on overparameterized or non-parametric models can excel at explaining complex nonlinear signals, but real-time updates for such models pose a challenging task. In this paper, we propose a neural process-based approach that addresses this trade-off. It encodes available observations within a CM signal into a representation space and then reconstructs the signal's history and evolution for prediction. Once trained, the model can encode an arbitrary number of observations without requiring retraining, enabling on-the-spot real-time predictions along with quantified uncertainty and can be readily updated as more online data is gathered. Furthermore, our model is designed to incorporate qualitative information (i.e., labels) from individual units. This integration not only enhances individualized predictions for each unit but also enables joint inference for both signals and their associated labels. Numerical studies on both synthetic and real-world data in reliability engineering highlight the advantageous features of our model in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.

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Section: Related Workmentioning

confidence: 99%

Federated Condition Monitoring Signal Prediction With Improved Generalization

Chung

Kontar

2024

IEEE Trans. Rel.

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Learning thermodynamically constrained equations of state with uncertainty

Sharma,

Gaffney,

Tsapetis

et al. 2024

APL Machine Learning

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Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material’s thermodynamic state variables—specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived, and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty is reduced by considering thermodynamic constraints.

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Data-level transfer learning for degradation modeling and prognosis

Fallahdizcheh

Wang

2022

Journal of Quality Technology

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Nonlinear Online Multioutput Gaussian Process for Multistream Data Informatics

Cited by 9 publications

References 21 publications

Federated Condition Monitoring Signal Prediction With Improved Generalization

Federated Condition Monitoring Signal Prediction With Improved Generalization

Learning thermodynamically constrained equations of state with uncertainty

Data-level transfer learning for degradation modeling and prognosis

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