An integration method of a hybrid genetic algorithm and the Levenberg-Marquardt algorithm for ultrasonic testing

• Autorzy: Li Xiang , Jia Jiuhong , Yang Dongxu , Gu Yiqing

Identyfikatory

DOI

10.24425/mms.2024.148536

• Języki publikacji: EN

Abstrakty

EN

The accurate measurement of time-of-flight (TOF) is essential in ultrasonic testing. Further, noise interference is the key factor affecting the measurement accuracy. Therefore, to develop a reliable computational method of TOF for test pieces working in noisy environments, an integration method of a hybrid genetic algorithm and the Levenberg-Marquardt algorithm (GA-LM) for ultrasonic thickness measurement is proposed in the present research. A Gaussian model is first established for an echo signal. Further, the model-based parameter estimation is converted into a nonlinear optimization problem by applying the least square method. As the parameter estimation methods are easily affected by the initial value, an integrating innovation of the GA-LM algorithm is proposed. The initial values of the model parameters are selected by GA to obtain an approximate global optimal solution. Subsequently, this approximate solution is used as the initial value for the LM algorithm to perform iterations. The accurate global optimal solution of the Gaussian model is obtained through these iterations. Finally, the measuring accuracy and robustness of the GA-LM algorithm for TOF computation are verified by both numerical simulation and experiment data.

Słowa kluczowe

EN

time-of-flight; parameter estimation; Genetic algorithm; Levenberg-Marquardt algorithm; measuring accuracy

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2024
• Tom: Vol. 31, nr 1
• Strony: 165--177
• Opis fizyczny: Bibliogr. 24 poz., rys., tab., wykr., wzory

Twórcy

autor

Li Xiang

• Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

autor

Jia Jiuhong

• Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

autor

Yang Dongxu

• Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

autor

Gu Yiqing

• Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Bibliografia

• [1] Song, J., Guo, D., Jia, J., & Tu, S. (2022). A new on-line ultrasonic thickness monitoring system for high temperature pipes. International Journal of Pressure Vessels and Piping, 199, 104691. https://doi.org/10.1016/j.ijpvp.2022.104691
• [2] Dos Santos, J. C. F., Pinheiro, P. P., & de França, J. A. (2019). Recovering of Corrupted Ultrasonic Waves, for Determination of TOF Using the Zero-Crossing Detection Technique. IEEE Transactions on Instrumentation and Measurement, 68(11), 4234-4241. https://doi.org/10.1109/TIM.2018.2890326
• [3] Zhou, L., Liu, H., Lian, M., Ying, Y., Li, T., & Wang, Y. (2018). Highly accurate adaptive TOF determination method for ultrasonic thickness measurement. Measurement Science and Technology, 29(4), 045002. https://iopscience.iop.org/article/10.1088/1361-6501/aa9acf/meta
• [4] Kim, Y. H., Song, S. J., & Lee, J. K. (2005). Technique for measurements of elastic wave velocities and thickness of solid plate from access on only one side. Japanese Journal of Applied Physics, 44(7R), 5240. https://iopscience.iop.org/article/10.1143/JJAP.44.5240/meta
• [5] Jenot, F., Ouaftouh, M., Duquennoy, M., & Ourak, M. (2001). Corrosion thickness gauging in plates using Lamb wave group velocity measurements. Measurement Science and Technology, 12(8), 1287. https://iopscience.iop.org/article/10.1088/0957-0233/12/8/341/meta
• [6] Queiros, R., Alegria, F. C., Girao, P. S., & Serra, A. C. C. (2010). Cross-correlation and sine-fitting techniques for high-resolution ultrasonic ranging. IEEE Transactions on Instrumentation and Measurement, 59(12), 3227-3236. https://doi.org/10.1109/TIM.2010.2047305
• [7] Demirli, R., & Saniie, J. (2001). Model-based estimation of ultrasonic echoes. Part I: Analysis and algorithms. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control, 48(3), 787-802. https://doi.org/10.1109/58.920713
• [8] Gholami, A., Honarvar, F., & Moghaddam, H. A. (2017). Modeling the ultrasonic testing echoes by a combination of particle swarm optimization and Levenberg-Marquardt algorithms. Measurement Science and Technology, 28(6), 065001. https://iopscience.iop.org/article/10.1088/1361-6501/aa61b6/meta
• [9] Jiao, Y., Li, Z., Zhu, J., Xue, B., & Zhang, B. (2022). ABIDE: A Novel Scheme for Ultrasonic Echo Estimation by Combining CEEMD-SSWT Method with EM Algorithm. Sustainability, 14(4), 1960. https://doi.org/10.3390/su14041960
• [10] Lu, Z., Yang, C., Qin, D., Luo, Y., & Momayez, M. (2016). Estimating ultrasonic time-of-flight through echo signal envelope and modified Gauss Newton method. Measurement, 94, 355-363. https://doi.org/10.1016/j.measurement.2016.08.013
• [11] Wang, X., Zhang B., Gao Y., Jiao Y., Li Z., & Zhu J. (2018). Ultrasonic flight speed measurement method based on nonlinear echo envelope model. Transducer and Microsystem Technologies, 37(12), 4.
• [12] Cheong, K. H., & Koh, J. M. (2019). A hybrid genetic-Levenberg Marquardt algorithm for automated spectrometer design optimization. Ultramicroscopy, 202, 100-106. https://doi.org/10.1016/j.ultramic.2019.03.004
• [13] Koh, J. M., & Cheong, K. H. (2019). Data-driven computational method for determining accurate analytical field solutions on arbitrary-geometry spectrometers. Ultramicroscopy, 202, 173-179. https://doi.org/10.1016/j.ultramic.2019.04.003
• [14] Koh, J. M., & Cheong, K. H. (2018). Automated electron-optical system optimization through switching Levenberg-Marquardt algorithms. Journal of Electron Spectroscopy and Related Phenomena, 227, 31-39. https://doi.org/10.1016/j.elspec.2018.05.009
• [15] Jackson, J. C., Summan, R., Dobie, G. I., Whiteley, S. M., Pierce, S. G., & Hayward, G. (2013). Time-of-flight measurement techniques for airborne ultrasonic ranging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control, 60(2), 343-355. https://doi.org/10.1109/TUFFC.2013.2570
• [16] Lu, Z., Ma, F., Yang, C., & Chang, M. (2020). A novel method for Estimating Time of Flight of ultrasonic echoes through short-time Fourier transforms. Ultrasonics, 103, 106104. https://doi.org/10.1016/j.ultras.2020.106104
• [17] Chang, M., Lu, Z., Huang, Q., & Yang, C. (2022). Parameter estimation for ultrasonic echo signals through improved matching pursuit and flower pollination algorithms. Measurement, 194, 111010. https://doi.org/10.1016/j.measurement.2022.111010
• [18] Zhang, T., Wang, X., Chen, Y., Shuai, Y., Ullah, Z., Ju, H., & Zhao, Y. (2019). Geomagnetic detection method for pipeline defects based on CEEMDAN and WEP-TEO. Metrology and Measurement Systems, 26(2). http://dx.doi.org/10.24425/mms.2019.128363
• [19] Kren, X., Feng H., & Yang Z. (2020). Selection of initial values for ultrasonic echo parameter estimation. Acta Acustica, 45(5), 11. https://www.jac.ac.cn/cn/article/doi/10.15949/j.cnki.0371-0025.2020.05.012
• [20] Wang, L., Hu, S., Liu, K., Chen, B., Wu, H., Jia, J., & Yao, J. (2020). A hybrid Genetic Algorithm and Levenberg-Marquardt (GA-LM) method for cell suspension measurement with electrical impedance spectroscopy. Review of Scientific Instruments, 91(12), 124104. https://doi.org/10.1063/5.0029491
• [21] Garg, H. (2019). A hybrid GSA-GA algorithm for constrained optimization problems. Information Sciences, 478, 499-523. https://doi.org/10.1016/j.ins.2018.11.041
• [22] Barszcz, T., & Zabaryłło, M. (2022). Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization. Metrology and Measurement Systems, 29(1). https://journals.pan.pl/dlibra/publication/138551/edition/122789/content
• [23] Mirjalili, S. (2019). Evolutionary algorithms and neural networks. In Studies in Computational Intelligence (Vol. 780). Berlin/Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-319-93025-1
• [24] Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80, 8091-8126. https://doi.org/10.1007/s11042-020-10139-6

Uwagi

1. This work is supported by the Natural Science Foundation of China (No. 52175138).

2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).