Article Article
Quantitative Analysis of Aeroengine Turbine Disk Surface Crack under Natural Magnetic Field

Aiming at surface cracks of aeroengine turbine disk, a weak magnetic detection technology without excitation is proposed. Signal processing is performed using blind source separation (BSS). Support vector machine (SVM) is used to realize quantitative evaluation. Four nickel-base superalloy test blocks with defects are designed. The magnetic induction intensity of the test block is collected by a weak magnetic detection instrument. Then, the signal processing based on BSS is carried out. Finally, SVM is used to quantitatively analyze the weak magnetic signal before and after BSS. Results show that SVM estimation accuracy of defect length, width, and depth is 90%, 94%, and 71% after BSS, respectively. These findings are 14%, 8%, and 23% higher than the estimation accuracy prior to BSS.

DOI: https://doi.org/10.1080/09349847.2022.2105458

References

1. D. Hu et al., Eng Fract Mech 196, 71–82 (2018). DOI: 10.1016/j.engfracmech.2018.03.019.

2. G. Chen et al., Mater Sci Eng A 655, 175–182 (2016). DOI: 10.1016/j.msea.2015.12.096.

3. Z. Zhou et al., Jixie Gongcheng Xuebao/Journal Mech Eng 53(12), 28–34 (2017). DOI: 10.3901/JME.2017.12.028.

4. N. G. Shmelev et al., Russ J Nondestruct Test 48(1), 15–22 (2012). DOI: 10.1134/S1061830912010093.

5. X. Guan et al., J Nondestruct Eval 33, 51–61 (2014). DOI: 10.1007/s10921-013-0202-z.

6. W. Dongsheng, K. S. Y. L. Xu, and L. Shuoming, 6th Youth Sci Technol Forum CAAC 5, 138–142 (2014). http://www.csaa.org.cn.

7. Q. Haiyan, R. E. N. Xuedong, and S. C. T. Yiwei, J Aeronaut Mater 36, 92–96 (2016). DOI:10.11868/j.1005-5053.

8. M. Genest and G. Li Inspection of aircraft engine components using induction thermography Can Conf Electr Comput Eng 2018 2018-May 10.1109/CCECE.2018.8447832

9. H. Niknazar, A. M. Nasrabadi, and M. B. Shamsollahi, Signal Process 183, 108045 (2021). DOI:10.1016/j.sigpro.2021.108045.

10. Z. Yuan, C. Ying, and Z. Y. Chen Hao, Chinese J Geophys 62, 572–586 (2019). https://doi.org/10.6038.

11. M. Rakibul Mowla et al., Biomed Signal Process Control 22, 111–118 (2015). DOI: 10.1016/j.bspc.2015.06.009.

12. M. Zoulikha and M. Djendi, Appl Acoust 112, 192–200 (2016). DOI: 10.1016/j.apacoust. 2016.05.012.

13. M. Khosravy et al., Comput Commun 157, 423–433 (2020). DOI: 10.1016/j.comcom.2020.04.042.

14. Q. Yi et al., NDT E Int 102, 264–273 (2019). DOI: 10.1016/j.ndteint.2018.12.010.

15. K. Wang et al., Meas 157, 107653 (2020). DOI: 10.1016/j.measurement.2020.107653.

16. P. Shi, S. Su, and Z. Chen, J Nondestruct Eval 39(2), 43 (2020). DOI: 10.1007/s10921-020-00688-z. 172 P. FU ET AL.

17. J.-W. Kim and S. Park, Sens 18 (2018). DOI: 10.3390/s18010109.

18. Z. Qiu et al., Insight - Non-Destructive Test Cond Monit 61(2), 90–94 (2019). DOI: 10.1784/insi.2019.61.2.90.

19. Z. Qiu, Z. Ruilei, and W. Zhang, Int J Manuf Res 12(2), 165 (2017). DOI: 10.1504/IJMR.2017.085417.

20. W. Han, Y. Yuan, and Y. Zhang, Fire Control Command Control 43, 80–84 (2018). DOI: 10.3969/j.1002-0640.2018.01.016.

21. W. Han and J. Xu, Fire Control Command Control 40, 88–91 (2015).

22. M. Layouni, M. Hamdi, and S. Tahar, Appl Soft Comput. (2016). DOI: 10.1016/j.asoc.2016.10.040.

23. L. Wang and Z. Chen, Int J Appl Electromagn Mech 64(1–4), 721–728 (2020). DOI: 10.3233/JAE-209383.

24. P. Shi, K. Jin, and X. Zheng, Int J Mech Sci 124, 229–241 (2017). DOI: 10.1016/j.ijmecsci.2017.03.001.

25. B. Hu, Y. Liu, and R. Yu, Meas 151, 107185 (2020). DOI: 10.1016/j.measurement.2019.107185.

26. B. Hu, Y. Liu, and R. Yu, Nondestruct Test Eval 9759 (2020). DOI: 10.1080/10589759.2020.1723583.

27. B. Ma et al., Digit Signal Process 112, 103007 (2021). DOI:10.1016/j.dsp.2021.103007.

28. Y. Guo, Y. Zhou, and Z. Zhang, Meas 171, 108513 (2020). DOI: 10.1016/j.measurement.2020.108513.

29. B. Hu, R. Yu, and W. Xu, Acta Aeronautica Et Astronautica Sinica 36, 3450–3455 (2015). DOI: 10.7527/S1000-6893.2015.0173.

30. R. Ali and Y. Cha, Constr Build Mater 226, 376–387 (2019). DOI: 10.1016/j.conbuildmat.2019.07.293.

31. H. Chen et al., NDT & E Int 130, 102657 (2022). DOI: 10.1016/j.ndteint.2022.102657.

32. H. Yun et al., Meas 186, 110155 (2021). DOI: 10.1016/j.measurement.2021.110155.

33. O. Avci et al., Mech Syst Signal Pr 147, 107077 (2021). DOI: 10.1016/j.ymssp.2020.107077.

34. Y. Cha and Z. Wang, Struct Health Monit 17(2), 313–324 (2018). DOI: 10.1177/1475921717691260.

35. A. Barbado, Ó. Corcho, and R. Benjamins, Expert Syst Appl 189, 116100 (2022). DOI: 10.1016/j.eswa.2021.116100.

36. S. Lee, Reliab Eng Syst Saf 209, 107481 (2021). DOI:10.1016/j.ress.2021.107481.

Metrics
Usage Shares
Total Views
13 Page Views
Total Shares
0 Tweets
13
0 PDF Downloads
0
0 Facebook Shares
Total Usage
13