Skip to main content
SpringerLink
Log in

Intelligent Ultrasonic Image Classification of Artillery Cradle Weld Defects Based on MECF-QPSO-KELM Method

  • ACOUSTIC METHODS
  • Published:
Russian Journal of Nondestructive Testing Aims and scope Submit manuscript

Abstract

The precise qualitative evaluation on the ultrasonic testing of the artillery cradle weld defects can effectively eliminate its security risk induced by the potential hazardous defects. Nevertheless, it is difficult to determine the detailed reflection properties of ultrasonic waves because of the effect of defect size, shape, orientation and its surface roughness. This study focuses on the intelligent analysis on the ultrasonic testing image of the artillery cradle weld defect. An intelligent classification method was proposed based on the small sample conditions. Thus, in this article, a significance classification feature evaluation algorithm was first proposed based on the multiple evaluation criteria fusion (MECF). No matter which pattern recognition algorithm was used, using the classification feature set (M7, unevenness of gray scale distribution, differential moment, standard deviation of entropy, directionality, long run advantage, contrast, standard deviation of energy, mixed entropy) to intelligently recognize the defect types has a high precision. Especially while using the kernel extreme learning machine (KELM), the classification accuracy reaches 96.4%. Thus, a multi-class classification model of the weld defect recognition termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) was further proposed. The corresponding classification accuracy was raised to 98%. Finally, comparative experiments were done with convolutional neural network (CNN) ResNet-34. The results show that compared with CNN ResNet-34, the proposed method exhibits obvious advantages, and more accurate classification results also indicate the proposed intelligent classification method can be used for the intelligent identification of artillery cradle weld defect during ultrasonic testing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.

REFERENCES

  1. Edwards, G.R., Inspection of welded joints, in: ASM Handbook. Welding, Brazing, and Soldering, Almere: ASM International, 1993.

    Google Scholar 

  2. Naddaf-Sh, M.M., Naddaf-Sh, S., Zargarzadeh, H., et al., Defect detection and classification in welding using deep learning and digital radiography, in: Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems, Cambridge: Academic, 2021, pp. 327–352.

    Google Scholar 

  3. Xu, H., Yan, Z.H., Ji, B.W., et al., Defect detection in welding radiographic images based on semantic segmentation methods, Measurement, 2022, vol. 188, p. 110569.

    Article  Google Scholar 

  4. Munir, N., Kim, H.J., Park, J., et al., Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions, Ultrasonics, 2019, vol. 94, pp. 74–81.

    Article  Google Scholar 

  5. Munir, N., Park, J., Kim, H.J., et al., Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder, NDT & E Int., 2020, vol. 111, pp. 102218.

  6. Liao, T.W., Improving the accuracy of computer-aided radiographic weld inspection by feature selection, NDT & E Int., 2009, vol. 42, no. 4, pp. 229–239.

    Article  CAS  Google Scholar 

  7. Cruz, F.C., Simas Filho, E.F., Albuquerque, M.C.S., et al., Efficient feature selection for neural network based detection of flaws in steel welded joints using ultrasound testing, Ultrasonics, 2017, vol. 73, pp. 1–8.

    Article  CAS  Google Scholar 

  8. Ferreira, G.R.B., de Castro Ribeiro, M.G., Kubrusly, A.C., et al., Improved feature extraction of guided wave signals for defect detection in welded thermoplastic composite joints, Measurement, 2022, p. 111372.

  9. Sambath, S., Nagaraj, P., and Selvakumar, N., Automatic defect classification in ultrasonic NDT using artificial intelligence, J. Nondestr. Eval., 2011, vol. 30, no. 1, pp. 20–28.

    Article  Google Scholar 

  10. Polikar, R. and Udpa, L., Frequency invariant classification of ultrasonic weld inspection signals, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 1998, vol. 45, no. 3, pp. 614–625.

    Article  CAS  Google Scholar 

  11. Ma, X.D., Zhang, P.L., Xu, T.P., et al., Research on weld defects detection based on ultrasonic phased array, Hot Working Technol., 2020, vol. 49, no. 5, pp. 150–154.

    Google Scholar 

  12. Huang, M. and Li, G., The study on the intelligent defect recognition methods in the ultrasonic non-destructive test of welds, J. Beijing Inform. Sci. Technol. Univ., 2009, vol. 24, no. 2, pp. 33–36.

    CAS  Google Scholar 

  13. Theresa Cenate, C.F., Sheela Rani, B., Ramadevi, R., et al., Optimization of the cascade feed forward back propagation network for defect classification in ultrasonic images, Russ. J. Nondestr. Test., 2016, vol. 52, no. 10, pp. 557–568.

    Article  Google Scholar 

  14. Fei, C., Han, Z., and Dong, J., An ultrasonic flaw-classification system with wavelet-packet decomposition, a mutative scale chaotic genetic algorithm, and a support vector machine and its application to petroleum-transporting pipelines, Russ. J. Nondestr. Test., 2006, vol. 42, no. 3, pp. 190–197.

    Article  Google Scholar 

  15. Singh, S. and Malik, K., Feature selection and classification improvement of Kinnow using SVM classifier, Meas. Sens., 2022, vol. 24, p. 100518.

  16. Tuba, E., Strumberger, I., Bezdan, T., et al., Classification and feature selection method for medical datasets by brain storm optimization algorithm and support vector machine, Procedia Comput. Sci., 2019, vol. 162, pp. 307–315.

    Article  Google Scholar 

  17. Zhao, Y., Zhu, W., Wei, P., et al., Classification of Zambian grasslands using random forest feature importance selection during the optimal phenological period, Ecol. Indic., 2022, vol. 135, p. 108529.

    Article  Google Scholar 

  18. Shi, J.F. and Bei, S., Research on image recognition based on invariant moment and SVM, 2010 First Int. Conf. Pervasive Comput. Sign. Proces. Appl., Harbin, 2010.

  19. Al-Ataby, A., Al-Nuaimy, W., Brett, C.R., et al., Automatic detection and classification of weld flaws in TOFD data using wavelet transform and support vector machines, Insight Nondestr. Test. Cond. Monit., 2010, vol. 52, no. 11, pp. 597–602.

    Article  Google Scholar 

  20. Chen, Y., Chen, B., Yao, Y., et al., A spectroscopic method based on support vector machine and artificial neural network for fiber laser welding defects detection and classification, NDT & E Int., 2019, vol. 108, p. 102176.

    Article  Google Scholar 

  21. Malarvel, M. and Singh, H., An autonomous technique for weld defects detection and classification using multi-class support vector machine in X-radiography image, Optik, 2021, vol. 231, p. 166342.

    Article  CAS  Google Scholar 

  22. Huang, Y., Yang, D., Wang, K., et al., A quality diagnosis method of GMAW based on improved empirical mode decomposition and extreme learning machine, J. Manuf. Proces., 2020, vol. 54, pp. 120–128.

    Article  Google Scholar 

  23. Lamberti, W.F., Blood cell classification using interpretable shape features: A Comparative study of SVM models and CNN-Based approaches, Comput. Meth. Programs Biomed. Upd., 2021, vol. 1, p. 100023.

  24. Sindhuja, S., Chakkaravarthy, D.M., and Selvam, J., Fuzzy ELM-based optimal spectrum sensing in CR-IoT network, Measurement: Sensors, 2023, vol. 25, p. 100561.

    Google Scholar 

  25. Moinuddin, S.Q., Hameed, S.S., Dewangan, A.K., et al., A study on weld defects classification in gas metal arc welding process using machine learning techniques, Mater. Today Proc., 2021, vol. 107.

    Book  Google Scholar 

  26. Dabov, K., Foi, A., Katkovnik, V., et al., Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space, 2007 IEEE Int. Conf. Image Proces., San Antonio, 2007.

  27. Pi, P. and Lima, D., Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis, Int. J. Cognit. Comput. Eng., 2021, vol. 2, pp. 93–103.

    Article  Google Scholar 

  28. Zhang, Y., Zhang, Y., and Zhang, B., Desktop dust detection algorithm based on gray gradient co-occurrence matrix, J. Comput. Appl., 2019, vol. 39, no. 8, pp. 2414–2419.

    Google Scholar 

  29. Lin, V., Bielecki, M., Yogendran, P., et al., Quantitative thermal imaging using grey-level run length matrix texture features correlate to radiation-induced skin toxicity, J. Med. Imag. Radiat. Sci., 2019, vol. 50, no. 2, pp. S6–S7.

    Article  Google Scholar 

  30. Haibing, H.U., Xue, Y., and Ting, X.U., et al., Image feature analysis of surface defects of ITO conductive film, J. Comput. Appl., 2017.

  31. Tamura, Hideyuki, Mori, et al., Textural features corresponding to visual perception, IEEE Trans. Syst. Man Cybern., 1978, vol. 8, no. 6, pp. 460–473.

    Article  Google Scholar 

  32. Wu, Z., Jiang, S., Zhou, X., et al., Application of image retrieval based on convolutional neural networks and Hu invariant moment algorithm in computer telecommunications, Comput. Commun., 2020, vol. 150, pp. 729–738.

    Article  Google Scholar 

  33. Bahassine, S., Madani, A., Al-Sarem, M., et al., Feature selection using an improved Chi-square for Arabic text classification, J. King Saud Univ. Comput. Inform. Sci., 2020, vol. 32, no. 2, pp. 225–231.

    Google Scholar 

  34. Ramesh, G., Madhavi, K., Reddy, P.D.K., et al., Improving the accuracy of heart attack risk prediction based on information gain feature selection technique, Mater. Today Proc., 2021.

    Book  Google Scholar 

  35. Jia, J., Yang, N., Zhang, C., et al., Object-oriented feature selection of high spatial resolution images using an improved Relief algorithm, Math. Comput. Model., 2013, vol. 58, nos. 3–4, pp. 619–626.

    Article  Google Scholar 

  36. Zhao, Y., Zhu, W., Wei, P., et al., Classification of Zambian grasslands using random forest feature importance selection during the optimal phenological period, Ecol. Indic., 2022, vol. 135, p. 108529.

    Article  Google Scholar 

  37. Yan, K. and Zhang, D., Feature selection and analysis on correlated gas sensor data with recursive feature elimination, Sens. Actuat. B Chem., 2015, vol. 212, pp. 353–363.

    Article  CAS  Google Scholar 

  38. Bai, B., Zhang, J., Wu, X., et al., Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems, Expert Syst. Appl., 2021, vol. 177, p. 114952.

    Article  Google Scholar 

  39. Gong, J., Yang, X., Wang, H., et al., Coordinated method fusing improved bubble entropy and artificial Gorilla Troops Optimizer optimized KELM for rolling bearing fault diagnosis, Appl. Acoust., 2022, vol. 195, p. 108844.

    Article  Google Scholar 

  40. Sun, J., Xu, W., and Feng, B., A global search strategy of quantum-behaved particle swarm optimization, IEEE Conf. Cybern. Intel. Syst., Singapore, 2004, vol. 1, pp. 111–116.

  41. Faseela, T. and Jayasree, M., Spoof face recognition in video using KSVM, Procedia Technol., 2016, vol. 24, pp. 1285–1291.

    Article  Google Scholar 

  42. Wong, W.K., Yuen, C.W.M., Fan, D.D., et al., Stitching defect detection and classification using wavelet transform and BP neural network, Expert Syst. Appl., 2009, vol. 36, no. 2, pp. 3845–3856.

    Article  Google Scholar 

Download references

Funding

This work was funded by the National Natural Science Foundation of China (NSFC 52075270), key scientific research projects and soft science research projects of military-civilian integration in Inner Mongolia Autonomous Region (JMZD202209), Science and Technology Plan Project of Inner Mongolia (2020GG0160), supported by Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (NJYT22063), Natural Science Foundation of Inner Mongolia (2022MS05006), and key R&D and achievement transformation plan projects in Inner Mongolia Autonomous Region (2022YFSJ0036, 2022YFSH0126).

Author information

Authors and Affiliations

  1. Inner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic Systems, Inner Mongolia University of Science and Technology, 014010, Baotou, Inner Mongolia Province, China

    Erqing Zhang, Shaofeng Wang & Bo Cheng

  2. Inner Mongolia Baotou Steel Xichuang Group Co., Ltd., 014033, Baotou, Inner Mongolia Province, China

    Shengrong Zhou

  3. Shanghai Aerospace Equipment Manufacturer Co., Ltd., Shanghai, China

    Shunzhou Huang

  4. University of Hertfordshire, College Lane, Hatfield, Herts, UK

    Wenbo Duan

Authors
  1. Erqing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaofeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shengrong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bo Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shunzhou Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wenbo Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaofeng Wang.

Ethics declarations

The authors declare that they have no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, E., Wang, S., Zhou, S. et al. Intelligent Ultrasonic Image Classification of Artillery Cradle Weld Defects Based on MECF-QPSO-KELM Method. Russ J Nondestruct Test 59, 305–319 (2023). https://doi.org/10.1134/S1061830922601088

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1061830922601088

Keywords:

Search

Navigation