Effect of Signal Features and Model Variables on Energy-Traced Arrival Time Picking of Acoustic Signals Used for Structural Damage Detection

Yıl 2024, Cilt: 28 Sayı: 2, 249 - 258, 30.04.2024

Sena Tayfur

https://doi.org/10.16984/saufenbilder.1226036

Öz

To monitor damage developments in structures, various structural health monitoring methods based on different principles are used. The common aspect of elastic wave-based methods is to place appropriate sensors on the structure, to detect acoustic wave propagation and to analyze these signals the sensors transformed. The arrival time of these recorded signals to the sensors is the most significant parameter used to determine critical information such as the time and location of the damage. Therefore, the accurate calculation of the arrival time affects the accuracy of the damage detection. In this study, effects of the signal-to-noise ratio (SNR), sampling frequency, length of the signal, and length of the focal window on determining the arrival time of the signals to the sensors were investigated. For this purpose, an energy-traced arrival time picking approach (Akaike Information Criterion, AIC), which is the frequently used method in the literature, has been applied to a typical acoustic signal originated from a concrete cracking. The results of the study suggest the necessity of noise elimination, the optimum level of data logging and the ratios of focal window lengths for accurate time of arrival detection in the field monitoring of the structures using acoustic methods.

Anahtar Kelimeler

Crack, Structural Health Monitoring, Signal, Arrival Time, Akaike Information Criteria (AIC)

Kaynakça

• [1] ASTM E 1316, “Standard Terminology for NDT”, 2002.
• [2] C. U. Grosse, M. Ohtsu, “Acoustic Emission Testing, Basics for Research-Applications in Civil Engineering, Springer-Verlag, Berlin, Heidelberg, 2008.
• [3] S. Tayfur, N. Alver, “Attenuation and Frequency Characteristics of Acoustic Waves in Steel and Synthetic Fiber-Reinforced Concrete: 3D-PCT and Unsupervised Pattern Recognition”, Applied Sciences, Vol. 12, 24, pp. 12976, 2022.
• [4] S. Tayfur, N. Alver, S. Abdi, S. Saatcı, A. Ghiami, “Characterization of concrete matrix/steel fiber de-bonding in an SFRC beam: Principal component analysis and k-mean algorithm for clustering AE data”, Engineering Fracture Mechanics, Vol. 194, pp. 73-85, 2018.
• [5] C. Van Steen, EVerstryng, “Signal-Based Acoustic Emission Clustering for Differentiation of Damage Sources in Corroding Reinforced Concrete Beams”, Applied Sciences, Vol. 12, 4, pp. 2154, 2022.
• [6] Z. Li, F. Li, X. S. Li, W. Yang, “Determination and AE Characterization of Concrete”, Journal of Engineering Mechanics, Vol. 126, 2, pp. 194-200, 2000.
• [7] S. Gollob, G. K. Kocur, T. Schumacher, L. Mhamdi, T. Vogel, “A novel multi-segment path analysis based on a heterogeneous velocity model for the localization of acoustic emission sources in complex propagation media”, Ultrasonics, Vol. 74, pp. 48-61, 2017.
• [8] F. Zhang, L. Pahlavan, Y. Yang, “Evaluation of acoustic emission source localization accuracy in concrete structures”, Structural Health Monitoring, Volume 19, 6, pp. 2063-2074, 2020.
• [9] Y. H. Lee, T. Oh, “The Measurement of P-, S-, and R-Wave Velocities to Evaluate the Condition of Reinforced and Prestressed Concrete Slabs”, Advances in Materials Science and Engineering, Vol. 2016, pp. 1-15, 2016.
• [10] X. Shang, Y. Wang, R. Miao, “Acoustic emission source location from P-wave arrival time corrected data and virtual field optimization method”, Mechanical Systems and Signal Processing, Vol. 163, pp. 108129, 2022.
• [11] J. H. Kurz, C. U. Grosse, H. W. Reinhardt, “Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete”, Ultrasonics, Vol. 43, 7, pp. 538-546, 2005. [12] A. Carpinteri, J. Xu, G. Lacidogna, A. Manuello, “Reliable onset time determination and source location of acoustic emissions in concrete structures”, Cement & Concrete Composites, Vol. 34, pp. 529-537, 2012.
• [13] F. Bai, D. Gagar, P. Foote, Y. Zhao, “Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals” Mechanical Systems and Signal Processing, Vol. 84, pp. 717-730, 2017.
• [14] M. Zhang, M. Li, H. Li, “Onset detection of ultrasonic signals for the testing of concrete foundation piles by coupled continuous wavelet transform and machine learning algorithms”, Advanced Engineering Informatics, Vol. 43, pp. 101034, 2020.
• [15] H. Akaike, “Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average”, Annuals of the Institute of Statistical Mathematics, Vol. 26, 1, pp. 363–387, 1974.
• [16] G. Kitagawa, H. Akaike, “A procedure for the modeling of non-stationary time series”, Annuals of the Institute of Statistical Mathematics, Vol. 30, 1, pp. 351-363, 1978.
• [17] R. Madarshahian, P. Ziehl, J. M. Caicedo, “Acoustic emission Bayesian source location: Onset time challenge”, Mechanical Systems and Signal Processing, Vol. 23, pp. 483-495, 2019.
• [18] H. Chen, Z. Yang, “Arrival Picking of Acoustic Emission Signals Using a Hybrid Algorithm Based on AIC and Histogram Distance”, IEEE Transactions on Instrumentation and Measurement, Vol. 70, pp. 3505808, 2020.
• [19] H. Li, Z. Yang, W. Yan, “An improved AIC onset-time picking method based on regression convolutional neural network”, Mechanical Systems and Signal Processing, Vol. 171, pp. 108867, 2022.
• [20] L. Geiger, “Probability method for the determination of earthquake epicentres from the arrival time only”, Bulletin of St. Louis University, Vol. 8, pp. 60-71.
• [21] V. Matz, R. Smid, S. Starman, M. Kreidl, “Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing”, Ultrasonics, Vol. 49, 8, pp. 752-759, 2009.
• [22] J. Xu, Z. Wang, C. Tan, L. Si, X. Liu, “A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm”, Applied Sciences, Vol. 7, 3, pp. 215, 2017.
• [23] K. Prajna, C. K. Mukhopadhyay, “Fractional Fourier Transform Based Adaptive Filtering Techniques for Acoustic Emission Signal Enhancement”, Journal of Nondestructive Evaluation, Vol. 39, 14, pp. 1-15, 2020.
• [24] C. Zhou, Y. Zhang Y, “Particle filter based noise removal method for acoustic mission signals”, Mechanical Systems and Signal Processing, Vol. 28, pp. 63-77, 2012.
• [25] M. Sraitih, Y. Jabrane, “A denoising performance comparison based on ECG Signal Decomposition and local means filtering”, Biomedical Signal Processing and Control, Vol. 69, pp. 102903, 2021.
• [26] N. Arı, S. Özen, Ö. H. Çolak, “Dalgacık Teorisi”, Palme Yayıncılık, Ankara, 2008.
• [27] İ. V. Öner, M. K. Yeşilyurt, E. Ç. Yılmaz, “Wavelet Analiz Tekniği ve Uygulama Alanları”, Ordu University Journal of Science and Technology, Vol. 7, 1, pp. 42-56, 2017.
• [28] R. Janeliukstis, “Continuous wavelet transform-based method for enhancing estimation of wind turbine blade natural frequencies and damping for machine learning purposes”, Measurement, Vol. 172, pp. 108897, 2021.
• [29] N. Wiener, “The Interpolation, Extrapolation and Smoothing of Stationary Time Series”, Report of the Services 19, Research Project DIC-6037 MIT, 1942.
• [30] M. Jayawardhana, X. Zhu, R. Liyanapathirana, “Damage Detection of Reinforced Concrete Structures Based on the Wiener Filter”, Australian Journal of Structural Engineering, Vol. 14, pp. 57-69, 2013.
• [31] Y. W. Chen, “Noise Reduction by Wiener Filter (https://github .com/JarvusChen/ MATLAB-Noise-Reduction-by-wiener-filter)”, GitHub. Retrieved April 22, 2022.
• [32] A. Savitzky, M. J. E. Golay, “Smoothing and Differentiation of Data by Simplified Least Squares Procedures”, Analytical Chemistry, Vol. 36, 8, pp. 1627-1639, 1964.
• [33] H. Hassanpour, “A time–frequency approach for noise reduction”, Digital Signal Processing, Vol. 18, 5, pp. 728-738, 2008.
• [34] H. Azami, K. Mohammadiand B. Bozorgtabar, “An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter”, Journal of Signal and Information Processing, Vol. 3, 1, pp. 39-44, 2012.