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Assembly tightness detection of bolt connections using gray-level images with high-order cumulants

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Abstract

Bolt connections are widely used in aero-engine rotors and gas turbines because of their load-transferring and detachable characteristics. However, bolts are prone to looseness due to the influence of fatigue, shock, and thermal loads, which decreases the reliability of the bolted connection structure. Therefore, detecting the assembly tightness of bolted connections is critical to ensure structural integrity during the assembly phase. A high-order cumulant-gray-level image feature (HOC-GLI) method is proposed to detect the assembly tightness of bolt connections. The core of this new method is to obtain high-order cumulant images of vibration signals to eliminate noise and reserve rich nonlinear information. The amplitude distribution of the third-order cumulant reflects the energy distribution of the vibration signal in 3D images. Then, the third-order cumulant 3D images are converted to 2D gray-level images to extract the texture feature. Finally, the root entropy index of the normalized gray-level cooccurrence matrix (GLCM) based on gray-level images is used to indicate the complexity of image information. Experimental studies on six bolt connection states of the aero-engine rotor are conducted. The relationship between the root entropy index and bolted connection status is obtained to verify the effectiveness of the proposed method for assembly tightness detection in different bolt connection statuses. The result shows that the root entropy index has a decreasing trend with the loosening of the bolts, which can quantitatively detect the assembly tightness of the bolt connection structure.

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References

  1. J. C. Zhang, X. N. Yang and J. Wang, On-line air-tightness and insertion loss simultaneous detection method of high air-tightness fiber optic penetration connector, 2015 International Conference on Optical Instruments and Technology: Optical Sensors and Applications, Beijing, China (2015).

  2. S. M. Y. Nikravesh and M. Goudarzi, A review paper on looseness detection methods in bolted structures, Latin American Journal of Solids and Structures, 14 (12) (2017) 2153–2176.

    Article  Google Scholar 

  3. F. R. Wang, S. C. M. Ho and G. B. Song, Modeling and analysis of an impact-acoustic method for bolt looseness identification, Mechanical Systems and Signal Processing, 133 (2019) 106249.

    Article  Google Scholar 

  4. F. R. Wang and G. B. Song, Bolt-looseness detection by a new percussion-based method using multifractal analysis and gradient boosting decision tree, Structural Health Monitoring-an International Journal, 19 (6) (2020) 2023–2032.

    Article  Google Scholar 

  5. Q. K. Li and X. J. Jing, Fault diagnosis of bolt loosening in structures with a novel second-order output spectrum-based method, Structural Health Monitoring-An International Journal, 19 (1) (2020) 123–141.

    Article  Google Scholar 

  6. P. Razi, R. A. Esmaeel and F. Taheri, Improvement of a vibration-based damage detection approach for health monitoring of bolted flange joints in pipelines, Structural Health Monitoring-An International Journal, 12 (3) (2013) 207–224.

    Article  Google Scholar 

  7. F. R. Wang and G. B. Song, A novel percussion-based method for multibolt looseness detection using one-dimensional memory augmented convolutional long short-term memory networks, Mechanical Systems and Signal Processing, 161 (2021) 107955.

    Article  Google Scholar 

  8. S. C. Cao, N. Guo and C. Xu, Robust damage localization in plate-type structures by using an enhanced robust principal component analysis and data fusion technique, Mechanical Systems and Signal Processing, 162 (2022) 108091.

    Article  Google Scholar 

  9. Q. K. Li and X. J. Jing, A novel second-order output spectrum based local tuning method for locating bolt-loosening faults, Mechanical Systems and Signal Processing, 147 (2021) 107104.

    Article  Google Scholar 

  10. L. T. Wang, B. Yuan and Z. B. Xu, Synchronous detection of bolts looseness position and degree based on fusing electromechanical impedance, Mechanical Systems and Signal Processing, 174 (2022) 109068.

    Article  Google Scholar 

  11. I. M. Lee, S. I. Han and H. J. Kim, Evaluation of rock bolt integrity using Fourier and wavelet transforms, Tunnelling and Underground Space Technology, 28 (2012) 304–314.

    Article  Google Scholar 

  12. X. S. Qin, C. Peng and G. Z. Zhao, Full life-cycle monitoring and earlier warning for bolt joint loosening using modified vibro-acoustic modulation, Mechanical Systems and Signal Processing, 162 (2022) 108054.

    Article  Google Scholar 

  13. N. Kaur, L. F. Li and S. Bhalla, A low-cost version of electromechanical impedance technique for damage detection in reinforced concrete structures using multiple piezo configurations, Advances in Structural Engineering, 20 (8) (2017) 1247–1254.

    Article  Google Scholar 

  14. D. M. Peairs, G. Park and D. J. Inman, Improving accessibility of the impedance-based structural health monitoring method, Journal of Intelligent Material Systems and Structures, 15 (2) (2004) 129–139.

    Article  Google Scholar 

  15. N. K. Mishra, P. K. Dash and N. Yashwanth, Damage monitoring of single lap bonded composite using acoustic emission technique, IOP Conference Series: Materials Science and Engineering, 455 (2018) 012026.

    Article  Google Scholar 

  16. K. Jian, N. Wang and Q. Li, Research on acoustic emission identification test method of single-tube tower model and base plate bolted connection, Journal of Southwest University of Science and Technology, 28 (3) (2013) 45–49.

    Google Scholar 

  17. J. W. Lee, Bolt-joint structural health monitoring technique using transfer impedance, Journal of Korea Academia-Industrial Cooperation Society, 20 (7) (2019) 387–392.

    Google Scholar 

  18. S. Wei and Z. Ying, Numerical simulation of bolt looseness monitoring based on impedance method, Applied Mechanics and Materials, 351–352 (2013) 1264–1268.

    Google Scholar 

  19. A. Soualhi, K. Medjaher and N. Zerhouni, Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression, IEEE Transactions on Instrumentation and Measurement, 64 (1) (2015) 52–62.

    Article  Google Scholar 

  20. W. Zhou, L. Xiao and W. Qu, Detection of bolt looseness in frame structures using empirical mode decomposition, Journal of Vibration and Shock, 35 (8) (2016) 201–206.

    Google Scholar 

  21. X. L. Zhang, Q. Yan and J. Yang, An assembly tightness detection method for bolt-jointed rotor with wavelet energy entropy, Measurement, 136 (2019) 212–224.

    Article  Google Scholar 

  22. S. K. Goumas, M. E. Zervakis and G. S. Stavrakakis, Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction, IEEE Transactions on Instrumentation and Measurement, 51 (3) (2002) 497–508.

    Article  Google Scholar 

  23. D. F. Shi, W. J. Wang and L. S. Qu, Defect detection for bearings using envelope spectra of wavelet transform, Journal of Vibration and Acoustics, 126 (4) (2004) 567–573.

    Article  Google Scholar 

  24. E. C. C. Lau and H. W. Ngan, Detection of motor bearing outer raceway defect by wavelet packet transformed motor current signature analysis, IEEE Transactions on Instrumentation and Measurement, 59 (10) (2010) 2683–2690.

    Article  Google Scholar 

  25. L. Fucai, M. Guang and Y. Lin, Wavelet transform-based higher-order statistics for fault diagnosis in rolling element bearings, Journal of Vibration and Control, 14 (11) (2008) 1691–1709.

    Article  MATH  Google Scholar 

  26. E. Orosco, P. Diez and E. Laciar, On the use of high-order cumulant and bispectrum for muscular-activity detection, Biomedical Signal Processing and Control, 18 (2015) 325–333.

    Article  Google Scholar 

  27. Y. J. Shen, S. Yang and J. Wang, Application of higher-order cumulant in fault diagnosis of rolling bearing, Journal of Physics: Conference Series, 448 (2013) 012008.

    Google Scholar 

  28. W. Mu, L. Shi and Y. Cai, Diesel engine fault diagnosis based on the global and local features fusion of time-frequency image, Journal of Vibration and Shock, 37 (10) (2018).

  29. S. Zhang, Y. Cai and L. Shi, Coding feature extraction and diagnosis of i.c. engine vibration time-frequency images, Mechanical Science and Technology for Aerospace Engineering, 37 (3) (2018) 391–395.

    Google Scholar 

  30. X. X. Ding, Q. B. He and Y. M. Shao, Transient feature extraction based on time-frequency manifold image synthesis for machinery fault diagnosis, IEEE Transactions on Instrumentation and Measurement, 68 (11) (2019) 4242–4252.

    Article  Google Scholar 

  31. W. Dou, Z.-S. Liu and Z.-X. Wang, Vibration fault diagnosis method based on gray level-gradient co-occurrence matrix for rotating machinery, Journal of Aerospace Power, 23 (10) (2008) 1939–1943 (in China).

    Google Scholar 

  32. H. Shen, H. Zhao and J. Mei, Diesel engine fault diagnosis based on high-order cumulant image features, Journal of Vibration and Shock, 34 (11) (2015) 133–138.

    Google Scholar 

  33. H. Rujiang, O. An, L. U. Wenxiu and C. H. U. Fulei, Review of diagnosis of rolling element bearings defaults by means of acoustic emission technique, Journal of Vibration and Shock, 27 (3) (2008) 75–79.

    Google Scholar 

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Acknowledgments

This research is supported by Shaanxi Provincial Natural Science Basic Research Program (Grant No. 2021JM-169), joint funding from the Equipment Pre-research Department of the Ministry of Education of the People’s Republic of China (Grant No. 6141A02033111) and Youth Talent Promotion Project Shaanxi Association of Science and Technology (Grant No.20170509).

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Authors and Affiliations

  1. Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, Chang’an University, Xi’an, 710064, P.R. China

    Yong Xia, Xiaoli Zhang, Qiang Yan & Yong Xiao

Authors
  1. Yong Xia
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  2. Xiaoli Zhang
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  3. Qiang Yan
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  4. Yong Xiao
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Corresponding author

Correspondence to Xiaoli Zhang.

Additional information

XiaoLi Zhang received a Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China in 2011. Currently, she is an Associate Professor in the Department of Mechanical and Electronic Engineering, School of Construction Machinery, Chang’an University, Xi’an, China. Her research interests include signal processing, deep learning, machinery condition monitoring, and intelligent fault diagnosis.

Yong Xia received a master’s degree in mechanical engineering from the Chang’an University of Xi’an, China in 2022. He is currently an algorithm engineer with TCL Tongli Electronics Co., Ltd., China. His research interests include speech recognition, signal processing, deep learning, and fault diagnosis.

Qiang Yan received a master’s degree in mechanical engineering from the Chang’an University of Xi’an, China in 2019. He is currently a vocational college teacher in Sanmenxia Social Management Vocational College. His research interests include fault diagnosis and signal processing.

Xiao Yong received a bachelor’s degree from Hunan University of Arts and Science, ChangDe, China in 2017. He is currently pursuing a master’s degree with the School of Engineering Machinery, Chang’an University, Xi’an, China. His current research interests include fault diagnosis and signal processing.

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Xia, Y., Zhang, X., Yan, Q. et al. Assembly tightness detection of bolt connections using gray-level images with high-order cumulants. J Mech Sci Technol 37, 4981–4988 (2023). https://doi.org/10.1007/s12206-023-0905-8

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  • DOI: https://doi.org/10.1007/s12206-023-0905-8

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