OJCM  Vol.6 No.1 , January 2016
On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network Process
Abstract: This article investigates the potential impact of manufacturing uncertainty in composite structures here in the form of thickness variation in laminate plies, on the robustness of commonly used Artificial Neural Networks (ANN) in Structural Health Monitoring (SHM). Namely, the robustness of an ANN SHM system is assessed through an airfoil case study based on the sensitivity of delamination location and size predictions, when the ANN is imposed to noisy input. In light of the observed poor performance of the original network, even when its architecture was carefully optimized, it had been proposed to weigh the input layer of the ANN by a set of signal-to-noise (SN) ratios and then trained the network. Both damage location and size predictions of the latter SHM approach were increased to above 90%. Practical aspects of the proposed robust SN-ANN SHM have also been discussed.
Cite this paper: Teimouri, H. , Milani, A. , Seethaler, R. and Heidarzadeh, A. (2016) On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network Process. Open Journal of Composite Materials, 6, 28-39. doi: 10.4236/ojcm.2016.61004.

[1]   Boller, C. and Meyendorf, N. (2008) State-of-the-Art in Structural Health Monitoring for Aeronautics. Proceedings of International Symposium on NDT in Aerospace, Fürth/Bavaria, Germany, 3-5 December 2008.

[2]   Balageas, D., Fritzen, C.P. and Gumes, A. (2006) Structural Health Monitoring. Antony Rowe Ltd., Chippenham, Wiltshire.

[3]   Perez, I., DiUlio, M., Maley, S. and Phan, N. (2010) Structural Health Monitoring in the Navy. International Journal of Structural Health Monitoring, 9, 199-209.

[4]   Lopez-Higuera, J.M. (2002) Introduction to Optical Fiber Sensor Technology. In: Lopez-Higuera, J.M., Ed., Handbook of Optical Fibre Sensing Technology, Wiley, New York, 1-21.

[5]   Teimouri, H., Milani, A.S. and Seethaler, R. (2013) On the Effect of Fabrication and Testing Uncertainties in Structural Health Monitoring. In: Silva, M., Ed., Design of Experiments Applications, InTech, Croatia.

[6]   Kesavan, A., John, S. and Herszberg, I. (2008) Strain Based Structural Health Monitoring of Complex Composite Structures. Structural Health Monitoring, 7, 1-13.

[7]   Kachlakev, D.I. (1998) Finite Element Method (FEM) Modeling for Composite Strengthening/Retrofit of Bridges. Research Project Work Plan, Civil, Construction and Environmental Engineering Department, Oregon State University, Corvallis, Oregon.

[8]   Kachlakev, D.I. (1998) Strengthening Bridges Using Composite Materials. FHWA-OR-RD-98-08, Oregon Department of Transportation, Salem, Oregon.

[9]   Kachlakev, D.I. and McCurry Jr., D. (2000) Simulated Full Scale Testing of Reinforced Concrete Beams Strengthened with FRP Composites: Experimental Results and Design Model Verification. Oregon Department of Transportation, Salem, Oregon.

[10]   Teimouri, H., Milani, A.S., Seethaler, R., Abedian, A., Heidarzadeh, A. and Teimouri, B. (2013) Towards Strain-Based Structural Health Monitoring of Composite Airfoil under Uncertainty. 19th International Conference on Composite Materials, Montreal, Canada, July-August 2013, 1-8.

[11]   Gurney. K. (1997) An Introduction to Neural Networks. University of Sheffield, UK.

[12]   Chu, F., Yuan, S. and Peng, Z. (2009) Machine Learning Techniques. Encyclopedia of Structural Health Monitoring.

[13]   Azzam, H. (1997) A Practical Approach for the Indirect Prediction of Structural Fatigue from Measured Flight Parameters. Proceeding of the Institution of Mechanical Engineering, Part G: Journal of Aerospace Engineering, 211, 29-38.

[14]   Azzam, H. (1997) The Use of Mathematical Models and Artificial Intelligent Techniques to improve Hums Prediction Capabilities. Proceedings of the Royal Aeronautical Society, Innovation in Rotorcraft Technology Conference, London, 24-25 June 1997, 16.1-16.14.

[15]   Azzam, H., Hebden, I., Gill, M., Beavan, F. and Wallace, M. (2005) Fusion and Decision Making Techniques for Structural Prognosis Health Management. IEEE Aerospace Conference, Montana, MT, 5-12 March 2005, Paper #1535.

[16]   Wallace, M., Azzam, H. and Newman, S. (2004) Indirect Approaches to Individual Aircraft Structural Monitoring. Proceeding of the Institution of Mechanical Engineering, Part G: Journal of Aerospace Engineering, 218, 329-346.

[17]   Reed, S.C. and Cole, D.G. (2003) Development of a Parametric Aircraft Fatigue Monitoring System Using Artificial Neural Network. Proceedings of the 22nd Symposium of the International Committee on Aeronautical Fatigue, Lucern, 9 May 2003, 47-63.

[18]   Scallonila, G., Cracia, J., Cabrejas, J. and Armijo, J.I. (2007) A Full-Scale Parametric Based Fatigue Monitoring System Using Neural Networks. Proceedings of the 24th Symposium of the International Committee on Aeronautical Fatigue, Naples, 16-18 May 2007.

[19]   Levinski, O. (2001) Australian Defense Science and Technology Organization. Prediction of Buffet Loads Using Artificial Neural Network, Document DSTO-RR-0218.

[20]   Teimouri, H. (2015) A New Statistical Approach to Strain-Based Structural Health Monitoring of Composites under Uncertainty. PhD Dissertation in Mechanical Engineering, University of British Columbia, BC, Canada.

[21]   Fowlkes, W.Y. and Creveling, C.M. (2013) Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development. Prentice Hall, Englewood Cliffs.

[22]   Welvaert, M. and Rosseel, Y. (2013) On the Definition of Signal-to-Noise Ratio and Contrast-to-Noise Ratio for fMRI Data. PLoS ONE, 8, e77089.

[23]   Griffantia, L., Baglioa, F., Pretia, M.G., Cecconic, P., Rovarisd, M., Basellib, G. and Laganà, M.M. (2012) Signal-to- Noise Ratio of Diffusion Weighted Magnetic Resonance Imaging: Estimation Methods and in Vivo Application to Spinal Cord. Biomedical Signal Processing and Control, 7, 285-294.

[24]   Poungponsri, S. and Yu, X.H. (2013) An Adaptive Filtering Approach for Electrocardiogram (ECG) Signal Noise Reduction Using Neural Networks. Neurocomputing, 117, 206-213.

[25]   Liu, A., Lu, M. and Wei, M. (1997) Structure Noise Reduction of Ultrasonic Signals Using Artificial Neural Network Adaptive Filtering. Ultrasonics, 35, 325-328.

[26]   Zou, L., Wang, Z. and Huang, J. (2007) Prediction of Subcellular Localization of Eukaryotic Proteins Using Position- Specific Profiles and Neural Network with Weighted Inputs. Journal of Genetics and Genomics, 34, 1080-1087.

[27]   Chen, T., Xu, X. and Wang, S. (2011) An Intelligent Prediction Method Based on Information Entropy Weighted Elman Neural Network. Proceedings of the Intelligent Computing and Information Science: International Conference, Chongqing, 8-9 January 2011, Part II, 142-147.

[28]   Fang, X., Luo, H. and Tang, J. (2005) Structural Damage Detection Using Neural Network with Learning Rate Improvement. Computers and Structures, 83, 2150-2161.

[29]   Kesavan, A., John, A. and Herszberg, I. (2008) Strain-Based Structural Health Monitoring of Complex Composite Structures. Structural Health Monitoring, 7, 203-213.

[30]   Al-Haik, M.S., Hussaini, M.Y. and Garmestani, M. (2006) Prediction of Nonlinear Viscoelastic Behaviour of Polymeric Composites Using an Artificial Neural Network. International Journal of Plasticity, 22, 1367-1392.

[31]   Koker, R., Altinkok, N. and Demir, A. (2006) Neural Network Based Prediction of Mechanical Properties of Particulate Reinforced Metal Matrix Composites Using Various Training Algorithms. Materials & Design, 28, 616-627.

[32]   Singh, A.P., Kamal, T.S. and Kumar, S. (2005) Virtual Curve Tracer for Estimation of Static Response Characteristics of Transducers. Measurement, 38, 166-175.

[33]   Haykin, S. (1997) Neural Networks—A Comprehensive Foundation. 2nd Edition, Prentice-Hall Inc., Upper Saddle River.