Model-based Damage Detection through Physics Guided Learning

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Published Nov 24, 2021
Ali I. Ozdagli Xenofon Koutsoukos

Abstract

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). https://doi.org/10.36001/phmconf.2021.v13i1.3012
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Keywords

physics-guided neural network, intermediate variable layer, interpretability

References
[Bakhary et al., 2007] Norhisham Bakhary, Hong Hao, and Andrew J Deeks. Damage detection using artificial neural network with consideration of uncertainties. Engineering Structures, 29(11):2806–2815, 2007.
[Balageas et al., 2010] Daniel Balageas, Claus-Peter Fritzen, and Alfredo Guemes.¨ Structural health monitoring, volume 90. John Wiley & Sons, 2010.
[Chen et al., 1996] SY Chen, Ming-Shaung Ju, and YG Tsuei. Estimation of mass, stiffness and damping matrices from frequency response functions. Journal of Vibration and Acoustics, 118(1):78–82, 1996.
[Craig Jr and Kurdila, 2006] Roy R Craig Jr and Andrew J Kurdila. Fundamentals of structural dynamics. John Wiley & Sons, 2006.
[Farrar and Worden, 2012] Charles R Farrar and Keith Worden. Structural health monitoring: a machine learning perspective. John Wiley & Sons, 2012.
[Figueiredo et al., 2009] Eloi Figueiredo, Gyuhae Park, Joaquim Figueiras, Charles Farrar, and Keith Worden. Structural health monitoring algorithm comparisons using standard data sets. Los Alamos National Laboratory, Los Alamos, NM, Report No. LA-14393, 2009.
[Fritzen, 1986] Claus-Peter Fritzen. Identification of Mass, Damping, and Stiffness Matrices of Mechanical Systems. Journal of Vibration, Acoustics, Stress, and Reliability in Design, 108(1):9–16, 01 1986.
[Gardner et al., 2020] P Gardner, X Liu, and K Worden. On the application of domain adaptation in structural health monitoring. Mechanical Systems and Signal Processing, 138:106550, 2020.
[Ghanem and Shinozuka, 1995] Roger Ghanem and Masanobu Shinozuka. Structural-system identification. i: Theory. Journal of Engineering Mechanics, 121(2):255–264, 1995.
[Hernandez-Garcia et al., 2010] Miguel R HernandezGarcia, Sami F Masri, Roger Ghanem, Eloi Figueiredo, and Charles R Farrar. An experimental investigation of change detection in uncertain chain-like systems. Journal of Sound and Vibration, 329(12):2395–2409, 2010.
[Jaishi and Ren, 2006] Bijaya Jaishi and Wei-Xin Ren. Damage detection by finite element model updating using modal flexibility residual. Journal of sound and vibration, 290(1-2):369–387, 2006.
[Jia et al., 2018] Xiaowei Jia, Anuj Karpatne, Jared Willard,
Michael Steinbach, Jordan Read, Paul C Hanson, Hilary A Dugan, and Vipin Kumar. Physics guided recurrent neural networks for modeling dynamical systems: Application to monitoring water temperature and quality in lakes. arXiv preprint arXiv:1810.02880, 2018.
[Karpatne et al., 2017] Anuj Karpatne, William Watkins, Jordan Read, and Vipin Kumar. Physics-guided neural networks (pgnn): An application in lake temperature modeling. arXiv preprint arXiv:1710.11431, 2017.
[Kim et al., 2003] Jeong-Tae Kim, Yeon-Sun Ryu, HyunMan Cho, and Norris Stubbs. Damage identification in beam-type structures: frequency-based method vs modeshape-based method. Engineering structures, 25(1):57– 67, 2003.
[Lin et al., 2017] Yi-zhou Lin, Zhen-hua Nie, and Hong-wei Ma. Structural damage detection with automatic featureextraction through deep learning. Computer-Aided Civil and Infrastructure Engineering, 32(12):1025–1046, 2017.
[McKenna et al., 2010] Frank McKenna, Michael H. Scott, and Gregory L. Fenves. Nonlinear finite-element analysis software architecture using object composition. Journal of Computing in Civil Engineering, 24(1):95–107, 2010.
[Muralidhar et al., 2019] Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh Tafti, and Anuj Karpatne. Physics-guided design and learning of neural networks for predicting drag force on particle suspensions in moving fluids. arXiv preprint arXiv:1911.04240, 2019.
[Sadoughi and Hu, 2019] Mohammadkazem Sadoughi and Chao Hu. Physics-based convolutional neural network for fault diagnosis of rolling element bearings. IEEE Sensors Journal, 19(11):4181–4192, 2019.
[Sun and Betti, 2015] Hao Sun and Raimondo Betti. A hybrid optimization algorithm with bayesian inference for probabilistic model updating. Computer-Aided Civil and Infrastructure Engineering, 30(8):602–619, 2015.
[Teughels and De Roeck, 2005] Anne Teughels and Guido De Roeck. Damage detection and parameter identification by finite element model updating. Revue europeenne´ de genie civil´ , 9(1-2):109–158, 2005.
[Wang et al., 1997] Z Wang, RM Lin, and MK Lim. Structural damage detection using measured frf data. Computer methods in applied mechanics and engineering, 147(12):187–197, 1997.
[Zhang and Sun, 2020] Zhiming Zhang and Chao Sun.
Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating. Structural Health Monitoring, page 1475921720927488, 2020.
[Zhang et al., 2020] Ruiyang Zhang, Yang Liu, and Hao Sun. Physics-guided convolutional neural network (phycnn) for data-driven seismic response modeling. Engineering Structures, 215:110704, 2020.
Section
Technical Papers

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