Article Article
Lamb Wave Detection and Localization of Multiple Discontinuities for Plate-Like Structures Based on DBSCAN and k-means

The use of lamb wave, a kind of ultrasonic guided wave in plate-like structures, is a promising tool for in-situ monitoring of metallic structures. In past research, various techniques have been applied to detect and locate a single discontinuity in plate-like structures. However, relatively few studies have been conducted for detecting multiple discontinuities. This paper proposes a novel technique using lamb wave detection for the localization of multiple discontinuities based on the density-based spatial clustering of applications with noise (DBSCAN) and the k-means algorithm. To verify the feasibility and effectiveness of the proposed technique, experiments using a circular piezoelectric sensor array were carried out for a single discontinuity, double discontinuities, and triple discontinuities. The experimental results show that the novel technique has the advantages of judging the number of discontinuities automatically and locating the discontinuities on plate-like structures accurately and effectively.

References

Agarwal, S., and M. Mitra, 2014, “Lamb Wave Based Automatic Damage Detection Using Matching Pursuit and Machine Learning,” Smart Mate-rials and Structures, Vol. 23, No. 8, doi: 10.1088/09641726/23/8/085012.

Albiruni, F., Y. Cho, J.-H. Lee, and B.-Y. Ahn, 2012, “Non-Contact Guided Waves Tomographic Imaging of Plate-Like Structures Using a Probabilistic Algorithm,” Materials Transactions, Vol. 53, No. 2, pp. 330–336.

Baochun, X., Y. Shenfang, W. Mulan, and Q. Lei, 2015, “Determining Impact Induced Damage by Lamb Wave Mode Extracted by EMD Method,” Measurement, Vol. 65, pp. 120–128.

Chen, S.-J., S.-P. Zhou, Y. Li, and L. Zhang, 2017a, “A Novel Defect Location Method for Plate-Like Structure by Using Forward-Scattering Wave and Fuzzy C-Means Clustering,” Proceedings of the ASME 2017 Pressure Vessels and Piping Conference, Vol. 5, 16–20 July 2017, Waikoloa, HI, doi: 10.1115/PVP2017-66236. 

Chen, S.-J., S.-P. Zhou, Y. Li, Y.-X. Xiang, and M.-X. Qi, 2017b, “Distance-Coefficient-Based Imaging Accuracy Improving Method Based on the Lamb Wave,” Chinese Physics Letters, Vol. 34, No. 4, pp. 55–59.

Chen, X., J.E. Michaels, and T.E. Michaels, 2014, “Load-enhanced Lamb Wave Techniques for Characterization of Scatterers in Structures with Complex Geometries,” Materials Evaluation, Vol. 72, No. 10, pp. 1314–1324.

Dubuc, B., A. Ebrahimkhanlou, and S. Salamone, 2017, “Sparse Recon-struction Localization of Multiple Acoustic Emissions in Large Diameter Pipelines,” Proceedings of SPIE 10168, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017, 12  April 2017, doi: 10.1117/12.2257505.

Ebrahimkhanlou, A., and S. Salamone, 2017, “A Probabilistic Framework for Single-Sensor Acoustic Emission Source Localization in Thin Metallic Plates,” Smart Materials and Structures, Vol. 26, No. 9, doi: 10.1088/1361-665X/aa78de.

Ebrahimkhanlou, A., B. Dubuc, and S. Salamone, 2016, “Damage Localiza-tion in Metallic Plate Structures Using Edge-Reflected Lamb Waves,” Smart Materials and Structures, Vol. 25, No. 8, doi: 10.1088/09641726/25/8/085035.

Ebrahimkhanlou, A., B. Dubuc, and S. Salamone, 2019, “A Generalizable Deep Learning Framework for Localizing and Characterizing Acoustic Emission Sources in Riveted Metallic Panels,” Mechanical Systems and Signal Processing, Vol. 130, pp. 248–272.

Harb, M.S., and F.G. Yuan, 2016, “Non-Contact Ultrasonic Technique for Lamb Wave Characterization in Composite Plates,” Ultrasonics, Vol. 64, pp. 162–169.

Jain, A.K., 2010, “Data Clustering: 50 Years Beyond K-Means,” Pattern Recognition Letters, Vol. 31, No. 8, pp. 651–666.

Kanungo, T., D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, and A.Y. Wu, 2002, “An Efficient K-Means Clustering Algorithm: Analysis and Implementation,” IEEE Transactions on Pattern Analysis and Machine Intel-ligence, Vol. 24, No. 7, pp. 881–892.

Krinidis, S., and V. Chatzis, 2010, “A Robust Fuzzy Local Information C-Means Clustering Algorithm,” IEEE Transactions on Image Processing, Vol. 19, No. 5, pp. 1328–1337.

Lee, L., B. Sheen, and Y. Cho, 2016, “Multi-Defect Tomographic Imaging with a Variable Shape Factor for the RAPID Algorithm,” Journal of Visuali-zation, Vol. 19, No. 3, pp. 393–402.

Liu, Z., F. Yu, R. Wei, C. He, and B. Wu, 2013, “Image Fusion Based on Single-frequency Guided Wave Mode Signals for Structural Health  Monitoring in Composite Plates,” Materials Evaluation, Vol. 71, No. 12,  pp. 1434–1443.

Liu, Z., T. Dong, Q. Peng, C. He, Q. Li, and B. Wu, 2018, “AE Source Localization in a Steel Plate with the Dispersive A0 Mode based on the Cross-Correlation Technique and Time Reversal Principle,” Materials Evaluation, Vol. 76, No. 3, pp. 371–382.

Lu, Y., L. Ye, Z. Su, L. Zhou, and L. Cheng, 2009, “Artificial Neural Network (ANN)-based Crack Identification in Aluminum Plates with Lamb Wave Signals,” Journal of Intelligent Material Systems and Structures, Vol. 20, No. 1, pp. 39–49.

Lu, Y., L. Ye, D. Wang, X. Wang, and Z. Su, 2010, “Conjunctive and Compromised Data Fusion Schemes for Identification of Multiple Notches in an Aluminium Plate Using Lamb Wave Signals,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Vol. 57, No. 9, 2010, pp. 2005–2016.

Pal, N.R., K. Pal, J.M. Keller, and J.C. Bezdek, 2005, “A Possibilistic Fuzzy C-Means Clustering Algorithm,” IEEE Transactions on Fuzzy Systems, Vol. 13, No. 4, pp. 517–530.

Perelli, A., L. De Marchi, E. Baravelli, A. Marzani, and N. Speciale, 2011, “Warped-Wigner-Hough Transformation of Lamb Waves for Automatic Defect Detection,” 2011 IEEE International Ultrasonics Symposium,  18–21 October 2011, Orlando, FL, doi: 10.1109/ULTSYM.2011.0266.

Ren, B., and C.J. Lissenden, 2015, “Phased Array Transducers for Ultra-sonic Guided Wave Mode Control and Identification for Aircraft Structural Health Monitoring,” Materials Evaluation, Vol. 73, No. 8, pp. 1089–1100.

Santos, M.J., A.R. Ferreira, and J.M. Perdigão, 2004, “Practical Considera-tions on Ultrasonic Guided Wave Propagation: Immersion and Contact Methods,” Materials Evaluation, Vol. 62, No. 4, pp. 443–449.

Su, Z., and L. Ye, 2004a, “An Intelligent Signal Processing and Pattern Recognition Technique for Defect Identification Using an Active Sensor Network,” Smart Materials and Structures, Vol. 13, No. 4, pp. 957–969.

Su, Z., and L. Ye, 2004b, “Lamb Wave-Based Quantitative Identification of Delamination in CF/EP Composite Structures Using Artificial Neural Algorithm,” Composite Structures, Vol. 66, Nos. 1–4, pp. 627–637.

Su, Z., and L. Ye, 2005, “Lamb Wave Propagation-Based Damage Identifi-cation for Quasi-Isotropic CF/EP Composite Laminates Using Artificial Neural Algorithm: Part II - Implementation and Validation,” Journal of Intelligent Material Systems and Structures, Vol. 16, No. 2, pp. 113–125.

Su, Z., X. Wang, Z. Chen, L. Ye, and D. Wang, 2006, “A Built-In Active Sensor Network for Health Monitoring of Composite Structures,” Smart Materials and Structures, Vol. 15, No. 6, pp. 1939–1949.

Tran, T.N., K. Drab, and M. Daszykowski, 2013, “Revised DBSCAN  Algorithm to Cluster Data with Dense Adjacent Clusters,” Chemometrics and Intelligent Laboratory Systems, Vol. 120, pp. 92–96.

Xu, B., and J. Ye, 2016, “Identification of Multi-Defects in Plate-like Struc-ture Based on Time-reversal,” Electronic Science and Technology, Vol. 29, No. 1, pp. 13–16.

Yang, S.-K., P.-H. Lee, C.-J. Huang, and J.-W. Cheng, 2010, “Wavelet Transform Analysis of Guided Wave Testing on Coated Pipes,” Materials Evaluation, Vol. 68, No. 11, pp. 1273–1284.

Zhang, Y., S. Huang, W. Zhao, and W. Shen, 2015, “Mode Recognition of Lamb Wave Testing Signals of Metal Plate’s Defects Based on STFT,”  Electrical Measurement and Instrumentation, Vol. 52, No. 4, pp. 19–23.

Zhou, A., S. Zhou, J. Cao, Y. Fan, and Y. Hu, 2000, “Approaches for Scaling DBSCAN Algorithm to Large Spatial Databases,” Journal of Computer Science and Technology, Vol. 15, No. 6, pp. 509–526.

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