Acoustic emission-based damage localization using wavelet-assisted deep learning

Mohamed Barbosh1, Kyle Dunphy1, Ayan Sadhu1
1Department of Civil and Environmental Engineering, Western University, London, Canada

Tóm tắt

Acoustic Emission (AE) has emerged as a popular damage detection and localization tool due to its high performance in identifying minor damage or crack. Due to the high sampling rate, AE sensors result in massive data during long-term monitoring of large-scale civil structures. Analyzing such big data and associated AE parameters (e.g., rise time, amplitude, counts, etc.) becomes time-consuming using traditional feature extraction methods. This paper proposes a 2D convolutional neural network (2D CNN)-based Artificial Intelligence (AI) algorithm combined with time–frequency decomposition techniques to extract the damage information from the measured AE data without using standalone AE parameters. In this paper, Empirical Mode Decomposition (EMD) is employed to extract the intrinsic mode functions (IMFs) from noisy raw AE measurements, where the IMFs serve as the key AE components of the data. Continuous Wavelet Transform (CWT) is then used to obtain the spectrograms of the AE components, serving as the “artificial images” to an AI network. These spectrograms are fed into 2D CNN algorithm to detect and identify the potential location of the damage. The proposed approach is validated using a suite of numerical and experimental studies.

Tài liệu tham khảo

Aggelis DG, Shiotani T, Terazawa M (2010) Assessment of construction joint effect in full-scale concrete beams by acoustic emission activity. J Eng Mech 136(7):906–912 Anay R, Cortez TM, Jáuregui DV, El Batanouny MK, Ziehl P (2016) On-site acoustic-emission monitoring for assessment of a Prestressed concrete double-tee-Beam bridge without plans. J Perform Constr Facil 30(4):04015062 Abouhussien AA, Hassan AA (2016) Acoustic emission-based analysis of bond behavior of corroded reinforcement in existing concrete structures. Struct Control Health Monit 24(3):e1893 Abdulazeez AM, Zeebaree DQ, Zebari DA, Zebari GM, Adeen IMN (2020) The applications of discrete wavelet transform in image processing: a review. J Soft Comput Data Mining 1(2):31–43 Benavent-Climent A, Gallego A, Vico JM (2011) An acoustic emission energy index for damage evaluation of reinforced concrete slabs under seismic loads. Struct Health Monit 11(1):69–81 Bahar O, Ramezani S (2012) Enhanced Hilbert Huang transform and its applications to modal identification. Struct Des Tall Special Build 23(4):239–253 Barbosh M, Singh P, Sadhu A (2020) Empirical mode decomposition and its variants: A review with applications in structural health monitoring. Smart Mater Struct 29(9):093001 Barbosh M, Sadhu A, Sankar G (2021) Time–frequency decomposition-assisted improved localization of proximity of damage using acoustic sensors. Smart Mater Struct 30(2):025021 Carpinteri A, Lacidogna G, Niccolini G (2010) Damage analysis of reinforced concrete buildings by the acoustic emission technique. Struct Control Health Monit 18(6):660–673 Calabrese L, Proverbio E (2020) A review on the applications of acoustic emission technique in the study of stress corrosion cracking. Corrosion Mater Degrad 2(1):1–33 Dunphy K, Sadhu A (2022) Autonomous crack detection approach for masonry structures using artificial intelligence. Recent Developments in Structural Health Monitoring and Assessment–Opportunities and Challenges: Bridges, Buildings and Other Infrastructures, World Scientific. pp 253–283 Ebrahimkhanlou A, Choi J, Hrynyk TD, Salamone S, Bayrak O (2020) Acoustic emission monitoring of containment structures during post-tensioning. Eng Struct 209:109930 Gilles J (2013) Empirical Wavelet Transform. IEEE Trans Signal Process 61(16):3999–4010 Hsueh Y-M, Ittangihal V, Wu W-B, Chang H-C, Kuo C-C (2019) Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform. Symmetry 11(10):1212 Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Math Phys Eng Sci 454(1971):903–995 Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (2002) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A 454:903–995 Kumar A, Zhou Y, Gandhi CP, Kumar R, Xiang J (2020) Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN). Alex Eng J 59:999–1012 Kralovec C, Schagerl M (2020) Review of structural health monitoring methods regarding a multi-sensor approach for damage assessment of metal and composite structures. Sensors 20(3):826 Li Z, Park HS, Adeli H (2016) New method for modal identification of super high-rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms. Struct Des Tall Spec Build 26(3):e1312 Liu Q, Huang C (2019) A Fault Diagnosis Method Based on Transfer Convolutional Neural Networks. IEEE Access 7:171423–171430 Li D, Wang Y, Yan WJ, Ren WX (2020) Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network. Struct Health Monit 20(4):1563–1582 Manthei G, Plenkers K (2018) Review on in situ acoustic emission monitoring in the context of structural health monitoring in mines. Appl Sci 8(9):1595 Ma G, Du Q (2020) Structural health evaluation of the prestressed concrete using advanced acoustic emission (AE) parameters. Construction and Building Materials 250:118860 Perez-Ramirez CA, Amezquita-Sanchez JP, Adeli H, Valtierra-Rodriguez M, Camarena-Martinez D, Romero-Troncoso RJ (2016) New methodology for modal parameters identification of smart civil structures using ambient vibrations and synchrosqueezed wavelet transform. Eng Appl Artif Intell 48:1–12 Pandhare V, Singh J, Lee J (2019) “Convolutional Neural Network Based Rolling-Element Bearing Fault Diagnosis for Naturally Occurring and Progressing Defects Using Time-Frequency Domain Features,” Prognostics and System Health Management Conference (PHM-Paris). France, Paris, pp 320–326 Quy TB, Kim J (2021) Crack detection and localization in a fluid pipeline based on acoustic emission signals. Mechanical Systems and Signal Processing 150:107254 Sarfarazi MP (1992) Acoustic emissions and damage constitutive characteristics of paper. Institute of paper science and technology Sadhu, A. (2013), “Decentralized ambient modal identification of structures.”, PhD Thesis, Department of Civil and Environmental Engineering, University of Waterloo. Sadhu A, Sony S, Friesen P (2019) Evaluation of progressive damage in structures using tensor decomposition-based wavelet analysis. J Vib Control 25(19–20):2595–2610 Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ICLR Conference 1409:1556 Singh P, Keyvanlou M, Sadhu A (2021) An improved time-varying empirical mode decomposition for structural condition assessment using limited sensors. Eng Struct 232:111882 Saeedifar M, Zarouchas D (2020) Damage characterization of laminated composites using acoustic emission: A review. Composit B Eng 195:108039 Shao S, Yan R, Lu Y, Wang P, Gao RX (2020) DCNN-based multi-signal induction motor fault diagnosis. IEEE Trans Instrum Meas 69(6):2658–2669 Sony S, Sadhu A (2020) Synchrosqueezing transform-based identification of time-varying structural systems using multi-sensor data. J Sound Vibrat 486:115576 Sony S, Dunphy K, Sadhu A, Capretz M (2021) A systematic review of convolutional neural network-based structural condition assessment techniques. Engineering Structures, Elsevier 226:111347 Sun G, Gao Y, Lin K, Hu Y (2019) Fine-grained fault diagnosis method of rolling bearing combining multisynchrosqueezing transform and sparse feature coding based on dictionary learning. Shock Vibrat 2019:1531079 Tang Z, Chen Z, Bao Y, Li H (2018) Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Struct Control Health Monit 26(1) Verstraete D, Ferrada A, Droguett EL, Meruane V, Modarres M (2017) Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock Vib 2017:1–17 Verstrynge E, Lacidogna G, Accornero F, Tomor A (2021) A review on acoustic emission monitoring for damage detection in masonry structures. Construct Build Mater 268:121089 Wang J, Mo Z, Zhang H, Miao Q (2019) A Deep Learning Method for Bearing Fault Diagnosis Based on Time-Frequency Image. IEEE Access 7:42373–42383 Worley R, Dewoolkar MM, Xia T, Farrell R, Orfeo D, Burns D, Huston DR (2019) Acoustic emission sensing for crack monitoring in prefabricated and Prestressed reinforced concrete bridge girders. J Bridg Eng 24(4):04019018 Wang Z, Ding K, Ren H, Ning J (2021). “Quantitative acoustic emission investigation on the crack evolution in concrete prisms by frequency analysis based on wavelet packet transform.”, Structural Health Monitoring, 147592172110188. Yuan M, Sadhu A, Liu K (2017) Condition assessment of structure with tuned mass damper using empirical wavelet transform. J Vib Control 24(20):4850–4867 Zhang Y, Xing K, Bai R, Sun D, Meng Z (2020) An enhanced convolutional neural network for bearing fault diagnosis based on time-frequency image. Measurement 157:107667