Deep Learning for Structural Health Monitoring: A Damage Characterization Application

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Soumalya Sarkar Kishore K. Reddy Michael Giering Michael Giering

Abstract

Structural health monitoring (SHM) is usually focused on damage detection (e.g., Yes/No) or approximate estimation of damage size. Any additional details of the damage such as configuration, shape, networking, geometrical statistics, etc., are often either ignored or significantly simplified during SHM characterization. These details, however, can be extremely important for understanding of damage severity and estimations of follow-up damage growth risk. To avoid expensive human participation and/or over-conservative SHM decisions, solutions of computational recognition for damage characterization are needed. Autonomous SHM from visual data is one of the significant challenges in the field of structural prognostics and health monitoring (PHM). The main shortcomings of the image-based PHM algorithms arise from the lack of robustness and fidelity to handle the variability of environment and nature of damage types. In recent times, deep learning has drawn huge amount of traction in the field of machine learning and visual pattern recognition due to its superior performance compared to the state-of-the-art techniques. This paper proposes to formulate and apply a deep learning technique to characterize the damage in the form of cracks on a composite material. The deep learning architecture is constructed by multi-layer neural network that is based on the fundamentals of unsupervised representational learning theory. The robustness and the accuracy of the approach is validated on an extensive set of real image data collected via applying variable load conditions on the structure. The paper has shown a high characterization accuracy over a wide range of loading conditions with limited number of labeled training image data.

How to Cite

Sarkar, S., Reddy, K. K., Giering, M., & Giering, M. (2016). Deep Learning for Structural Health Monitoring: A Damage Characterization Application. Annual Conference of the PHM Society, 8(1). https://doi.org/10.36001/phmconf.2016.v8i1.2544
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Keywords

structural health monitoring, deep learning, crack detection

References
Angulo, J., & Velasco-Forero, S. (2013). Stochastic morphological filtering and bellman-maslov chains. In Mathematical morphology and its applications to signal and image processing (pp. 171–182). Springer.
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8), 1798–1828.
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., et al. (2007). Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19, 153.
Buades, A., Coll, B., & Morel, J.-M. (2005). A non-local algorithm for image denoising. In Computer vision and pattern recognition, 2005. cvpr 2005. ieee computer society conference on (Vol. 2, pp. 60–65).
Cagnoni, S., Coppini, G., Rucci, M., Caramella, D., & Valli, G. (1993). Neural network segmentation of magnetic resonance spin echo images of the brain. Journal of biomedical engineering, 15(5), 355–362.
Chambon, S., Gourraud, C., Moliard, J. M., & Nicolle, P. (2010). Road crack extraction with adapted filtering and markov model-based segmentation: introduction and validation. In International joint conference on computer vision theory and applications, visapp.
Comer, M. L., & Delp, E. J. (2000, Oct). The em/mpm algorithm for segmentation of textured images: analysis and further experimental results. IEEE Transactions on Image Processing, 9(10), 1731-1744. doi: 10.1109/83.869185
Deng, L., & Dong, Y. (2014). Foundations and trends® in signal processing. Signal Processing, 7, 3–4.
Duval, L., Moreaud, M., Couprie, C., Jeulin, D., Talbot, H., & Angulo, J. (2014, Oct). Image processing for materials characterization: Issues, challenges
and opportunities. In 2014 ieee international conference on image processing (icip) (p. 4862-4866). doi: 10.1109/ICIP.2014.7025985
Erhan, D., Courville, A., & Bengio, Y. (2010). Understanding representations learned in deep architectures. Department d’Informatique et Recherche Operationnelle, University of Montreal, QC, Canada, Tech. Rep, 1355.
Gurvich, M. R., Clavette, P. L., & Robeson, M. E. (2016). Approach of Interlaminar Characterization for Thick Aircraft Composite Structures. In 57th
aiaa/asce/ahs/asc structures, structural dynamics, and materials conference, aiaa scitech, paper aiaa 2016 (pp. 1235–1243).
Hall, L. O., Bensaid, A. M., Clarke, L. P., Velthuizen, R. P., Silbiger, M. S., & Bezdek, J. C. (1992). A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. Neural Networks, IEEE Transactions on, 3(5), 672–682.
Krakow, W. (1982). Image-processing for materials characterization. In Journal of metals (Vol. 35, pp. A42–A42).
Leach, R. K. (2013). Characterisation of areal surface texture. Springer.
Liao, S., Gao, Y., Oto, A., & Shen, D. (2013). Representation learning: A unified deep learning framework for automatic prostate mr segmentation. In Medical image computing and computer-assisted intervention–miccai 2013 (pp. 254–261). Springer.
Oliveira, H., & Correia, P. L. (2009, Aug). Automatic road crack segmentation using entropy and image dynamic thresholding. In Signal processing conference, 2009 17th european (p. 622-626).
O¨ zkan,M., Dawant, B.M.,&Maciunas, R. J. (1993). Neuralnetwork- based segmentation of multi-modal medical images: a comparative and prospective study. Medical Imaging, IEEE Transactions on, 12(3), 534–544.
Park, C., Huang, J. Z., Ji, J. X., & Ding, Y. (2013, March). Segmentation, inference and classification of partially overlapping nanoparticles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3), 1-1. doi: 10.1109/TPAMI.2012.163
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., & Nielsen, M. (2013). Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In Medical image computing and computer-assisted intervention–miccai 2013 (pp. 246–253). Springer.
Reddy, K. K., Solmaz, B., Yan, P., Avgeropoulos, N. G., Rippe, D. J., & Shah, M. (2012, May). Confidence guided enhancing brain tumor segmentation in multiparametric mri. In 2012 9th ieee international symposium on biomedical imaging (isbi) (p. 366-369). doi: 10.1109/ISBI.2012.6235560
Robertson, I. M., Schuh, C. A., Vetrano, J. S., Browning, N. D., Field, D. P., Jensen, D. J., . . . others (2011). Towards an integrated materials characterization toolbox. Journal of Materials Research, 26(11), 1341–1383.
Ruggiero, C., Ross, A., & Porter, R. (2015). Segmentation and learning in the quantitative analysis of microscopy images. In Is&t/spie electronic imaging (pp. 94050L– 94050L).
Sarkar, S., Lore, K. G., & Sarkar, S. (2015). Early detection of combustion instability by neural-symbolic analysis on hi-speed video. In Proceedings of the nips workshop on cognitive computation: Integrating neural and symbolic approaches; 29th annual conference on neural information processing systems (nips 2015).
Sarkar, S., Lore, K. G., Sarkar, S., Ramanan, V., Chakravarthy, S. R., Phoha, S., & Ray, A. (2015). Early detection of combustion instability from hi-speed
flame images via deep learning and symbolic time series analysis. In Annual conference of the prognostics and health management society 2015.
Tomasi, C., & Manduchi, R. (1998). Bilateral filtering for gray and color images. In Computer vision, 1998. sixth international conference on (pp. 839–846).
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Technical Papers