single-jc.php

JACIII Vol.26 No.6 pp. 1053-1060
doi: 10.20965/jaciii.2022.p1053
(2022)

Paper:

Application of Bispectrum Dimensionality Reduction Method in Ultrasonic Echo Signal Processing

Jian Tang*,†, Wenxiu Yu*, Guoxin Zhao*, Xiangdong Jiao**, and Xuepeng Ding***

*School of Information Engineering, Beijing Institute of Petrochemical Technology
No.19 Qingyuan North Road, Daxing District, Beijing 102617, China

**School of Mechanical Engineering, Beijing Institute of Petrochemical Technology
No.19 Qingyuan North Road, Daxing District, Beijing 102617, China

***MCC Testing Certification Co., Ltd.
33 Xitucheng Road, Haidian District, Beijing 100088, China

Corresponding author

Received:
March 14, 2022
Accepted:
September 2, 2022
Published:
November 20, 2022
Keywords:
longdistance pipeline, ultrasonic echo, signal processing, higher-order spectrum, bispectrum dimensionality reduction
Abstract

Processing ultrasonic echo signals to obtain high-precision residual thickness information of the pipeline wall is the key to nondestructive testing of corrosion of a long-distance pipeline. The traditional power spectrum estimation method assumes that an analyzed echo signal is Gaussian, and the useful information is insufficiently extracted, which leads to errors in the processing results. In this paper, to solve this problem, the bispectrum, which requires the least amount of computation in higher-order spectral estimation, is proposed to process an echo signal with a non-minimum phase and non-Gaussian characteristics. The bispectrum is projected onto a one-dimensional frequency space using the dimensionality reduction method, and one-dimensional diagonal slices of the bispectrum are extracted to analyze the characteristics of the echo signal, which significantly improves the intuitiveness of data processing. The experimental results show that the bispectrum dimensionality reduction method has high accuracy in processing ultrasonic echo signals, and the relative error of the residua wall thickness is below 2%. A C-scan image displaying the shape, size, depth, and other characteristics of pipeline corrosion obtained by the proposed method is much better than that using the traditional power spectrum estimation method. Therefore, the proposed method is suitable for nondestructive testing of corrosion of long-distance pipelines.

Detailed flowchart of algorithm

Detailed flowchart of algorithm

Cite this article as:
J. Tang, W. Yu, G. Zhao, X. Jiao, and X. Ding, “Application of Bispectrum Dimensionality Reduction Method in Ultrasonic Echo Signal Processing,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.6, pp. 1053-1060, 2022.
Data files:
References
  1. [1] N. M. H. Basri, K. S. M. Sahari, and A. Anuar, “Development of a Robotic Boiler Header Inspection Device with Redundant Localization System,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, pp. 451-458, 2014.
  2. [2] A. Lanjile, M. Younis, S.-J. Kim, and S. Lee, “Exploiting Multi-Modal Sensing for Increased Detection Fidelity of Pipeline Leakage,” ASME 2019 Int. Design Engineering Technical Conf. and Computers and Information in Engineering Conf., Vol.2B, doi: 10.1115/DETC2019-97553, 2019.
  3. [3] M. S. M. Naqiuddin, M. S. Leong, L. M. Hee, and M. A. M. Azrieasrie, “Ultrasonic signal processing techniques for Pipeline: A review,” Engineering Application of Artificial Intelligence Conf. 2018, Vol.255, Article No.06006, 2019.
  4. [4] J. Tang, X.-D. Jiao, and B. Dai, “Application of improved secondary 1.5-D spectrum estimation in pipeline inner inspection,” J. of Shanghai Jiaotong University, Vol.49, No.3, pp. 406-410, 2015 (in Chinese).
  5. [5] R. Demirli and J. Saniie, “Model-based estimation of ultrasonic echoes. Part I: Analysis and algorithms,” IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control, Vol.48, No.3, pp. 787-802, 2001.
  6. [6] R. Demirli and J. Saniie, “Model-based estimation of ultrasonic echoes. Part II: Nondestructive evaluation applications,” IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control, Vol.48, No.3, pp. 803-811, 2001.
  7. [7] W. Lu and Y. Wen, “Time delay estimation based on cross-power spectrum for water pipeline leakage locating,” Chinese J. of Scientific Instrument, Vol.28, pp. 504-509, 2007 (in Chinese).
  8. [8] K. Sasaki, “An automatic determination of smoothing bandwidth in B-T method for power spectral estimation,” J. of Sound and Vibration, Vol.247, No.11, pp. 165-173, 2001.
  9. [9] S. Sheng, B. Dai, Z. Xie, and X. Cao, “Research on Technique of Intelligent Ultrasonic Pipeline Pig for Signal Processing,” J. of Beijing Institute of Petrochemical Technology, Vol.12, No.1, pp. 38-41, 2004 (in Chinese).
  10. [10] J. P. Burg, “A New Analysis Technique for Time Series Data,” Paper Presented at Advanced Study Institute on Signal Processing, NATO, Enschede, Netherlands, 1968.
  11. [11] J. Tang and B. Dai, “Research on Ultrasonic Measurement of Pipeline Thickness Based on Power Spectral Estimation,” J. of Beijing Institute of Petrochemical Technology, Vol.13, No.1, pp. 18-22, 2005 (in Chinese).
  12. [12] S. M. Kay and S. L. Marple, “Spectrum Analysis – A Modern Perspective,” Proc. of the IEEE, Vol.69, No.11, pp. 1380-1419, 1981.
  13. [13] L. Marple, “A New Autoregressive Spectrum Analysis Algorithm,” IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol.28, No.4, pp. 441-454, 1980.
  14. [14] J. Xiao and Z.-Z. Liu, “Analysis of radio signal higher order spectrum estimation,” Electronic Design Engineering, Vol.26, No.1, pp. 106-109, 2018 (in Chinese).
  15. [15] J.-X. Chen, Q. Wang, and Y.-Q. Xia, “Experimental method of estimating effective laser intensity by use of the high-order spectrum,” Chinese J. of Lasers, Vol.30, No.3, pp. 255-258, 2003.
  16. [16] A. A. Tabassam, M. U. Suleman, S. Khan, and S. H. R. Tirmazi, “Spectrum estimation and spectrum hole opportunities prediction for cognitive radios using higher-order statistics,” 2011 Wireless Advanced, pp. 213-217, 2011.
  17. [17] A. Joshi, L. Udpa, and S. Udpa, “Use of higher order statistics for enhancing magnetic flux leakage pipeline inspection data,” Proc. of the 12th Int. Symp. on Interdisciplinary Electromagnetic, Mechanic and Biomedical Problems, ISEM Bad Gastein, Vol.25, pp. 357-362, 2007.
  18. [18] D. Yang, W. Zhang, G. Xu, T. Li, J. Shen, Y. Yue, and S. Li, “Partial Discharge Pulse Segmentation Approach of Converter Transformers Based on Higher Order Cumulant,” Energies, Vol.15, No.2, Article No.415, 2022.
  19. [19] X. Mi, X. Chen, Z. Liu, Y. Liu, and Q. Liu, “Bispectrum feature recognition of radar signal based on entropy evaluation and modal decomposition,” Systems Engineering and Electronics, Vol.43, No.8, pp. 2116-2123, 2021 (in Chinese).
  20. [20] A. G. Barnett and R. C. Wolff, “A time-domain test for some types of nonlinearity,” IEEE Trans. on Signal Processing, Vol.53, No.1, pp. 26-33, 2005.
  21. [21] A. N. Shiryaev, “Probability,” Springer New York, 1984.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024