Skip to main content
SpringerLink
Log in

Classification of spring strain signals for road classes using Hilbert–Huang transform

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Kim YS, Kim JG (2017) Evaluation of corrosion fatigue and life prediction of lower arm for automotive suspension component. Met Mater Int 23:98–105

    Article  Google Scholar 

  2. Kong YS, Abdullah S, Schramm D, Omar MZ, Haris SM, Bruckmann T (2017) Mission profiling of road data measurement for coil spring fatigue life. Measurement 107:99–110

    Article  Google Scholar 

  3. Petracconi CL, Ferreira SE, Palma ES (2010) Fatigue life simulation of a rear tow hook assembly of a passenger car. Eng Fail Anal 17:455–463

    Article  Google Scholar 

  4. Mršnik M, Slavič J, Boltežar M (2013) Frequency-domain methods for a vibration-fatigue-life estimation–application to real data. Int J Fatigue 47:8–17

    Article  Google Scholar 

  5. Qiang R, Hongyan W (2011) Frequency domain fatigue assessment of vehicle component under random load spectrum. J Phys Conf Ser 305:012060

    Article  Google Scholar 

  6. Kvittem MI, Moan T (2014) Frequency versus time domain fatigue analysis of a semisubmersible wind turbine tower. J Offsh Mech Arct Eng 137:011901

    Article  Google Scholar 

  7. Mohammad M, Abdullah S, Jamaludin N, Innayatulah O (2018) Fatigue life assessment of SAE 1045 carbon steel under strain events using the weibull distribution. J Mech Eng 5:165–180

    Google Scholar 

  8. Miesowicz K, Staszewski WJ, Korbiel T (2016) Analysis of Barkhausen noise using wavelet-based fractal signal processing for fatigue crack detection. Int J Fatigue 83:109–116

    Article  Google Scholar 

  9. Antonino-Daviu JA, Riera-Guasp M, Pineda-Sanchez M, Perez RB (2009) A critical comparison between DWT and Hilbert–Huang-based methods for the diagnosis of rotor bar failures in induction machines. IEEE Trans Ind Appl 45:1794–1803

    Article  Google Scholar 

  10. Fan W, Wang X, Wang L, Yu Z (2016) The application of Hilbert-Huang transform energy spectrum in brushless direct current motor vibration signals monitoring of unmanned aerial vehicle. Adv Mech Eng 8:1–7

    Article  Google Scholar 

  11. Kabla A, Mokrani K (2016) Bearing fault diagnosis using Hilbert-Huang transform (HHT) and support vector machine (SVM). Mech Ind 17:308

    Article  Google Scholar 

  12. Silani M, Ziaei-Rad S, Talebi H (2013) Vibration analysis of rotating systems with open and breathing cracks. Appl Math Model 37(24):9907–9921

    Article  MathSciNet  MATH  Google Scholar 

  13. Du Y, Liu C, Wu D, Jiang S (2014) Measurement of international roughness index by using Z -axis accelerometers and GPS. Math Probl Eng 107:99–110

    Google Scholar 

  14. Zhang Z, Deng F, Huang Y, Bridgelall R (2015) Road roughness evaluation using in-pavement strain sensors. Smart Mater Struct 24:115029

    Article  Google Scholar 

  15. Reza-Kashyzadeh K, Ostad-Ahmad-Ghorabi MJ, Arghavan A (2014) Investigating the effect of road roughness on automotive component. Eng Fail Anal 41:96–107

    Article  Google Scholar 

  16. Kumar P, Lewis P, Mcelhinney CP, Rahman AA (2015) An algorithm for automated estimation of road roughness from mobile laser scanning data. Photogramm Rec 30:30–45

    Article  Google Scholar 

  17. Kang DK, Lee SH, Goo SH (2007) Development of standardization and management system for the severity of unpaved test courses. Sensors 7:2004–2027

    Article  Google Scholar 

  18. Fauriat W, Mattrand C, Gayton N, Beakou A, Cembrzynski T (2016) Estimation of road profile variability from measured vehicle responses. Veh Syst Dyn 54:585–605

    Article  Google Scholar 

  19. Bridgelall, R.; Huang, Y.; Zhang, Z.; Deng, F. Precision enhancement of pavement roughness localization with connected vehicles. Meas. Sci. Technol. 2016, 27.

  20. Libbrecht MW, Noble WS (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16:321–332

    Article  Google Scholar 

  21. Masino J, Pinay J, Reischl M, Gauterin F (2017) Road surface prediction from acoustical measurements in the tire cavity using support vector machine. Appl Acoust 125:41–48

    Article  Google Scholar 

  22. Tudón-Martínez JC, Fergani S, Sename O, Martinez JJ, Morales-Menendez R, Dugard L (2015) Adaptive road profile estimation in semiactive car suspensions. IEEE Trans Control Syst Technol 23:2293–2305

    Article  Google Scholar 

  23. Qin Y, Langari R, Wang Z, Xiang C, Dong M (2017) Road excitation classification for semi-active suspension system with deep neural networks. J Intell Fuzzy Syst 33:1907–1918

    Article  Google Scholar 

  24. Pawar P, Gawande S (2012) A comparative study on different types of approaches to text categorization. Int J Mach Learn Comput 2:423–426

    Article  Google Scholar 

  25. Lian C, Zhuge Y, Beecham S (2011) The relationship between porosity and strength for porous concrete. Constr Build Mater 25:4294–4298

    Article  Google Scholar 

  26. Putra TE, Abdullah S, Schramm D, Nuawi MZ, Bruckmann T (2015) Generating strain signals under consideration of road surface profiles. Mech Syst Signal Process 60:485–497

    Article  Google Scholar 

  27. Huang NE, Wu Z (2008) a Review on Hilbert-Huang transform: method and its applications. Rev Geophys 46:1–23

    Article  Google Scholar 

  28. Abdullah S, Abdullah MF, Zulkefli AS, Mazlan NH (2019) Numerical impact strain response of multi-layered steel-aluminium plate using signal processing. J Braz Soc Mech Sci Eng 41:29

    Article  Google Scholar 

  29. Han Y, Zhao Y, Yang R, Li T (2020) Monitoring operational status of electromechanical systems using audiovisual information fusion. J Braz Soc Mech Sci Eng 42:445

    Article  Google Scholar 

  30. Zhao X, Xiang H, Shi Z (2020) Piezoelectric energy harvesting from vehicles induced bending deformation in pavements considering the arrangement of harvesters. Appl Math Model 77:327–340

    Article  MATH  Google Scholar 

  31. Gharehchopogh FS, Khaze SR, Maleki I (2015) A new approach in bloggers classification with hybrid of k-nearest neighbor and artificial. Neural Netw Algorithms. 8:237–246

    Google Scholar 

  32. Klair A, Kaur R (2012) Software effort estimation using SVM and kNN. Psrcentre Org, pp 28–29

  33. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874

    Article  Google Scholar 

  34. Cho S, Shahriar MR, Chong U (2014) Identification of significant intrinsic mode functions for the diagnosis of induction motor fault. J Acoust Soc Am 136:72–77

    Article  Google Scholar 

  35. Chin CH, Abdullah S, Singh SSK, Ariffin AK, Schramm D (2020) Durability assessment of suspension coil spring considering the multifractality of road excitation. Measurement 158:107697

    Article  Google Scholar 

  36. Abdelkareem MAA, Xu L, Guo X, Ali MKA, Elagouz A, Hassan MA, Essa FA, Zou J (2018) Energy harvesting sensitivity analysis and assessment of the potential power and full car dynamics for different road modes. Mech Syst Signal Process 110:307–332

    Article  Google Scholar 

  37. Praznowski K., Mamala J (2016) Classification of the road surface condition on the basis of vibrations of the sprung mass in a passenger car. IOP conference series: materials science and engineering, vol 148

  38. Baccigalupi A, Liccardo A (2016) The Huang Hilbert Transform for evaluating the instantaneous frequency evolution of transient signals in non-linear systems. Meas J Int Meas Confed 86:1–13

    Article  Google Scholar 

  39. Icctp 2011 © asce 2011 2348. 2011, pp 2348–2357

  40. González A, O’Brien EJ, Li YY, Cashell K (2008) The use of vehicle acceleration measurements to estimate road roughness. Veh Syst Dyn 46:483–499

    Article  Google Scholar 

  41. Kropáč O, Múčka P (2009) Classification scheme for random longitudinal road unevenness considering road waviness and vehicle response. Shock Vib 16:273–289

    Article  Google Scholar 

  42. Li Y, Zhang X (2011) Improving k nearest neighbor with exemplar generalization for imbalanced classification. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 6635 LNAI, pp 321–332

  43. Ladds MA, Thompson AP, Kadar JP, Slip D, Hocking D, Harcourt R (2017) Super machine learning: Improving accuracy and reducing variance of behaviour classification from accelerometry. Anim Biotelemetry 5:1–9

    Article  Google Scholar 

  44. Xu C, Wang Y, Bao X, Li F (2018) Vehicle classification using an imbalanced dataset based on a single magnetic. Sensor 18:1–16

    Google Scholar 

  45. Wang G, Chen C, Yu S (2017) Robust non-fragile finite-frequency H 1 static output-feedback control for active suspension systems. Mech Syst Signal Process 91:41–56

    Article  Google Scholar 

  46. Kong YS (2017) Schramm D, Omar MZ, Mohd. Haris S, Abdullah S (2017) The significance to establish a durability model for an automotive ride

  47. Jahromi AF, Bhat RB, Xie WF (2014) Robust control of passenger cars with semi-active suspension system using magnetorheological shock absorber. Proc Can Soc, 1–6

  48. Tseng HE, Hrovat D (2015) State of the art survey: Active and semi-active suspension control. Veh Syst Dyn 53:1034–1062

    Article  Google Scholar 

Download references

Acknowledgements

The authors graciously acknowledge the financial support provided by the Ministry of Education (MOE) Malaysia and Universiti Kebangsaan Malaysia (Project No.: FRGS/1/2019/TK03/UKM/01/3) for this research.

Author information

Authors and Affiliations

  1. APM Engineering and Research Sdn Bhd, C-1-05, Block C, Oasis Square, No.2, Jalan 1A/7A, Ara Damansara, 47301, Petaling Jaya, Selangor, Malaysia

    Y. S. Kong

  2. Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia

    S. Abdullah & S. S. K. Singh

Authors
  1. Y. S. Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Abdullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. S. K. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. S. Kong.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Technical Editor: João Marciano Laredo dos Reis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kong, Y.S., Abdullah, S. & Singh, S.S.K. Classification of spring strain signals for road classes using Hilbert–Huang transform. J Braz. Soc. Mech. Sci. Eng. 44, 86 (2022). https://doi.org/10.1007/s40430-022-03390-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-022-03390-5

Keywords

Search

Navigation