Model-based Damage Detection through Physics Guided Learning



Published Nov 24, 2021
Ali I. Ozdagli Xenofon Koutsoukos


Data-driven learning approaches have gained a lot of interest in evaluating and validating complex dynamic systems. One of the main challenges for developing a reliable learning model is the lack of training data covering a large range of various operational conditions. Extensive training data can be generated using a physics-based simulation model. On the other hand, the learning algorithm should be still tested with experimental data obtained from the actual system. Modeling errors may lead to a statistical divergence between the simulation training data and the experimental testing data, causing poor performance, especially for domain-agnostic black-box learning methods. To close the gap between the simulation and experimental domains, this paper proposes a physics-guided learning approach that combines the power of the neural network with domain-specific physics knowledge. Specifically, the proposed architecture integrates physical parameters extracted from the physics-based simulation model into the intermediate layers of the neural network to constrain the learning process. To demonstrate the effectiveness of the proposed approach, the architecture is adopted to a damage classification problem for a three-story structure. Our results show that the accuracy for localizing the damage correctly based on experimental data improves significantly over black-box models, especially under large modeling errors. In this paper, we also use the physics-based intermediate layers to analyze the interpretability of the classification results.

How to Cite

Ozdagli, A. I., & Koutsoukos, X. (2021). Model-based Damage Detection through Physics Guided Learning. Annual Conference of the PHM Society, 13(1).
Abstract 818 | PDF Downloads 606



physics-guided neural network, intermediate variable layer, interpretability

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