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Monitoring of multi-bolt connection looseness using a novel vibro-acoustic method

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Abstract

Bolted connections are prone to losing their preloads with the increasing service life, thus inducing engineering accidents and economic losses in industries. Therefore, it is important to detect bolt loosening, while current structural health monitoring methods mainly focus on single-bolt joints, whose applications in industries are limited. Thus, in this paper, a novel vibro-acoustic modulation (VAM) method, is developed to detect looseness of the multi-bolt connection. Compared to traditional VAM, the proposed method uses linear swept sine waves for both low-frequency and high-frequency excitations, which avoids a priori knowledge of the structure. Moreover, the orthogonal matching pursuit method is applied to compress original modulated signals and exclude redundant features. Then, a new entropy, namely the Gnome entropy with acronym gEn, is proposed in this paper. According to simulation analysis, the gEn has better anti-noise capacity and fewer parameters than traditional entropy. Finally, after quantifying the dynamic characteristics of compressed signals to obtain feature sets through the gEn, we feed feature sets into a random forest classifier and achieve looseness detection of the multi-bolt connection. Moreover, the proposed method in this paper has great potential to detect other structural damages and provides guidance for further investigations on the VAM method.

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References

  1. Wang, F., Ho, S.C.M., Song, G.: Modeling and analysis of an impact-acoustic method for bolt looseness identification. Mech. Syst. Signal Process. 133, 106249 (2019)

    Google Scholar 

  2. Argatov, I., Sevostianov, I.: Health monitoring of bolted joints via electrical conductivity measurements. Int. J. Eng. Sci. 48(10), 874–887 (2010)

    Google Scholar 

  3. Amerini, F., Barbieri, E., Meo, M., Polimeno, U.: Detecting loosening/tightening of clamped structures using nonlinear vibration techniques. Smart Mater. Struct. 19(8), 085013 (2010)

    Google Scholar 

  4. Caccese, V., Mewer, R., Vel, S.S.: Detection of bolt load loss in hybrid composite/metal bolted connections. Eng. Struct. 26(7), 895–906 (2004)

    Google Scholar 

  5. Wang, F., Huo, L., Song, G.: A piezoelectric active sensing method for quantitative monitoring of bolt loosening using energy dissipation caused by tangential damping based on the fractal contact theory. Smart Mater. Struct. 27(1), 015023 (2018)

    Google Scholar 

  6. Wang, F., Chen, Z., Song, G.: Monitoring of multi-bolt connection looseness using entropy-based active sensing and genetic algorithm-based least square support vector machine. Mech. Syst. Signal Process. 136, 106507 (2020)

    Google Scholar 

  7. Wang, F., Ho, S.C.M., Huo, L., Song, G.: A novel fractal contact-electromechanical impedance model for quantitative monitoring of bolted joint looseness. IEEE Access 6, 40212–40220 (2018)

    Google Scholar 

  8. Kudela, P., Radzieński, M., Ostachowicz, W.: Impact induced damage assessment by means of Lamb wave image processing. Mech. Syst. Signal Process. 102, 3–36 (2018)

    Google Scholar 

  9. Sohn, H., Lim, H.J., Desimio, M.P., Brown, K., Derriso, M.: Nonlinear ultrasonic wave modulation for online fatigue crack detection. J. Sound Vib. 333, 1473–1484 (2014)

    Google Scholar 

  10. Ooijevaar, T., Rogge, M.D., Loendersloot, R., Warnet, L., Akkerman, R., Tinga, T.: Vibro-acoustic modulation-based damage identification in a composite skin-stiffener structure. Struct. Health Monit. 15(4), 458–472 (2016)

    Google Scholar 

  11. Klepka, A., Pieczonka, L., Staszewski, W.J., Aymerich, F.: Impact damage detection in laminated composites by non-linear vibro-acoustic wave modulations. Compos. Part B Eng. 65, 99–108 (2014)

    Google Scholar 

  12. Solodv, I.Y., Krohn, N., Busse, G.: CAN: an example of nonclassical acoustic nonlinearity in solids. Ultrasonics 40(1–8), 621–625 (2002)

    Google Scholar 

  13. Zhang, M., Shen, Y., Xiao, L., Qu, W.: Application of subharmonic resonance for the detection of bolted joint looseness. Nonlinear Dyn. 88(3), 1643–1653 (2017)

    Google Scholar 

  14. Yang, Y., Ng, C.T., Kotousov, A.: Bolted joint integrity monitoring with second harmonic generated by guided waves. Struct. Health Monit. 18(1), 193–204 (2019)

    Google Scholar 

  15. Jing, X.J., Li, Q.K.: A nonlinear decomposition and regulation method for nonlinearity characterization. Nonlinear Dyn. 83(3), 1355–1377 (2016)

    MathSciNet  MATH  Google Scholar 

  16. Li, Q.K., Jing, X.J.: A second-order output spectrum approach for fault detection of bolt loosening in a satellite-like structure with a sensor chain. Nonlinear Dyn. 89(1), 587–606 (2017)

    MATH  Google Scholar 

  17. Meyer, J.J., Adams, D.E.: Theoretical and experimental evidence for using impact modulation to assess bolted joints. Nonlinear Dyn. 81(1–2), 103–117 (2015)

    MathSciNet  MATH  Google Scholar 

  18. Meyer, J.J., Adams, D.E.: Using impact modulation to quantify nonlinearities associated with bolt loosening with applications to satellite structures. Mech. Syst. Signal Process. 116, 787–795 (2019)

    Google Scholar 

  19. Amerini, F., Meo, M.: Structural health monitoring of bolted joints using linear and nonlinear acoustic/ultrasound methods. Struct. Health Monit. 10(6), 659–672 (2011)

    Google Scholar 

  20. Zhang, Z., Liu, M., Liao, Y., Su, Z., Xiao, Y.: Contact acoustic nonlinearity (CAN)-based continuous monitoring of bolt loosening: Hybrid use of high-order harmonics and spectral sidebands. Mech. Syst. Signal Process. 103(2018), 280–294 (2018)

    Google Scholar 

  21. Li, N., Wang, F., Song, G.: New entropy-based vibro-acoustic modulation method for metal fatigue crack detection: an exploratory study. Measurement 150, 107075 (2019)

    Google Scholar 

  22. Wang, F., Ho, S.C.M., Song, G.: Monitoring of early looseness of multi-bolt connection: a new entropy-based active sensing method without saturation. Smart Mater. Struct. 28, 10LT01 (2019)

    Google Scholar 

  23. Nichols, J.M., Todd, M.D., Wait, J.R.: Using state space predictive modeling with chaotic interrogation in detecting joint preload loss in a frame structure experiment. Smart Mater. Struct. 12(4), 580–601 (2003)

    Google Scholar 

  24. Park, G., Cudney, H.H., Inman, D.J.: Feasibility of using impedance-based damage assessment for pipeline structures. Earthq. Eng. Struct. Dyn. 30(10), 1463–1474 (2001)

    Google Scholar 

  25. Min, J., Park, S., Yun, C.B., Lee, C.G., Lee, C.: Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity. Eng. Struct. 39, 210–220 (2012)

    Google Scholar 

  26. Liang, D., Yuan, S.F.: Decision fusion system for bolted joint monitoring. Shock Vib. 2015, 592043 (2015)

    Google Scholar 

  27. Fierro, G.P.M., Meo, M.: Structural health monitoring of the loosening in a multi-bolt structure using linear and modulated nonlinear ultrasound acoustic moments approach. Struct. Health Monit. 17(6), 1349–1364 (2018)

    Google Scholar 

  28. Wang, F., Song, G.: Bolt early looseness monitoring using modified vibro-acoustic modulation by time-reversal. Mech. Syst. Signal Process. 130, 349–360 (2019)

    Google Scholar 

  29. Dziedziech, K., Pieczonka, L., Adamczyk, M., Klepka, A., Staszewski, W.J.: Efficient swept sine chirp excitation in the non-linear vibro-acoustic wave modulation technique used for damage detection. Struct. Health Monit. 17(3), 565–576 (2018)

    Google Scholar 

  30. Yoder, N.C., Adams, D.E.: Vibro-acoustic modulation utilizing a swept probing signal for robust crack detection. Struct. Health Monit. 9(3), 257–267 (2010)

    Google Scholar 

  31. Yang, J., Zhang, Y., Zhu, Y.: Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mech. Syst. Signal Process. 21(5), 2012–2024 (2007)

    Google Scholar 

  32. Ibanez-Molina, A.J., Iglesias-Parro, S., Soriano, M.F., Aznarte, J.I.: Multiscale Lempel–Ziv complexity for EEG measures. Clin. Neurophysiol. 126(3), 541–548 (2015)

    Google Scholar 

  33. Udhayakumar, R.K., Karmakar, C., Palaniswami, M.: Approximate entropy profile: a novel approach to comprehend irregularity of short-term HRV signal. Nonlinear Dyn. 88(2), 823–837 (2017)

    MATH  Google Scholar 

  34. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039–2049 (2000)

    Google Scholar 

  35. Zhang, J., Hou, G., Cao, K., Ma, B.: Operation conditions monitoring of flood discharge structure based on variance dedication rate and permutation entropy. Nonlinear Dyn. 93(4), 2517–2531 (2018)

    Google Scholar 

  36. Wu, Y., Shang, P., Li, Y.: Modified generalized multiscale sample entropy and surrogate data analysis for financial time series. Nonlinear Dyn. 92(3), 1335–1350 (2018)

    Google Scholar 

  37. Rong, L., Shang, P.: Topological entropy and geometric entropy and their application to the horizontal visibility graph for financial time series. Nonlinear Dyn. 92(1), 41–58 (2018)

    Google Scholar 

  38. Gao, J., Hu, J., Tung, W.: Entropy measures for biological signal analyses. Nonlinear Dyn. 68(3), 431–444 (2012)

    MathSciNet  MATH  Google Scholar 

  39. Sun, W., Yan, D.: Identification of the nonlinear vibration characteristics in hydropower house using transfer entropy. Nonlinear Dyn. 75(4), 673–691 (2014)

    Google Scholar 

  40. Lopes, A.M., Machado, J.A.T.: Integer and fractional-order entropy analysis of earthquake data series. Nonlinear Dyn. 84(1), 79–90 (2016)

    MathSciNet  Google Scholar 

  41. Citi, L., Guffanti, G., Mainardi, L.: Rank-based multi-scale entropy analysis of heart rate variability. In: Proceeding of the Computing in Cardiology conference, pp. 597–600 (2014)

  42. Manis, G., Aktaruzzaman, M., Sassi, R.: Bubble entropy: an entropy almost free of parameters. IEEE T. Bio-Med. Eng. 64(11), 2711–2718 (2017)

    Google Scholar 

  43. Sarbazi-Azad, H.: Stupid sort: a new sorting algorithm. Newsl. (Comput. Sci. GLASGOW) 599, 4 (2000)

    Google Scholar 

  44. Liang, Z., Wang, Y., Sun, X., Li, D., Voss, L.J., Sleigh, J.W., Hagihira, S., Li, X.: EEG entropy measures in anesthesia. Front. Comput. Neurosci. 9, 1–17 (2015)

    Google Scholar 

  45. Unakafov, A.M., Keller, K.: Conditional entropy of ordinal patterns. Phys. D Nonlinear Phenom. 269, 94–102 (2014)

    MathSciNet  MATH  Google Scholar 

  46. Zheng, J., Pan, H., Cheng, J.: Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech. Syst. Signal Process. 85, 746–759 (2017)

    Google Scholar 

  47. Bearing Data Cente: Case Western Reserve University, (http://csegroups.case.edu/bearingdatacenter/pages/download-data-file)

  48. Zhang, Z., Xu, Y., Yang, J., Li, X., Zhang, D.: A survey of sparse representation: algorithms and applications. IEEE Access 3, 490–530 (2015)

    Google Scholar 

  49. Donoho, D.L.: Compressed sensing. IEEE T. Inf. Theory 52(4), 1289–1306 (2006)

    MathSciNet  MATH  Google Scholar 

  50. Donoho, D.L., Elad, M.: Optimally sparse representation in general (nonorthogonal) dictionaries via \(l^{1}\) minimization. Proc. Nat. Acad. Sci. USA 100(5), 2197–2202 (2003)

    MATH  Google Scholar 

  51. Candes, E.J., Tao, T.: Near-optimal signal recovery from random projections: universal encoding strategies? IEEE T. Inf. Theory 52(12), 5406–5425 (2006)

    MathSciNet  MATH  Google Scholar 

  52. Lyu, Q., Lin, Z., She, Y., Zhang, C.: A comparison of typical \(\text{ l }_{{\rm p}}\) minimization algorithms. Neurocomputing 119, 413–424 (2013)

    Google Scholar 

  53. Tropp, J.A., Gilbert, A.C., Strauss, M.J.: Algorithms for simultaneous sparse approximation: part I: greedy pursuit. IEEE T. Inf. Theory 53(12), 4655–4666 (2007)

    MATH  Google Scholar 

  54. Tropp, J.A.: Algorithms for simultaneous sparse approximation: part II: convex relaxation. Signal Process 86(3), 589–602 (2006)

    MATH  Google Scholar 

  55. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE T. Inf. Theory 53(12), 4655–4666 (2007)

    MathSciNet  MATH  Google Scholar 

  56. Shapire, R., Freund, Y., Bartlett, P., Lee, W.: Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Stat. 26(5), 1651–1686 (1998)

    MathSciNet  MATH  Google Scholar 

  57. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  58. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    MATH  Google Scholar 

  59. Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2, 18–22 (2002)

    Google Scholar 

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Acknowledgements

This research was partially supported by the China Scholarship Council (No. 201706060203), which is greatly appreciated.

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Correspondence to Gangbing Song.

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Wang, F., Song, G. Monitoring of multi-bolt connection looseness using a novel vibro-acoustic method. Nonlinear Dyn 100, 243–254 (2020). https://doi.org/10.1007/s11071-020-05508-7

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