Article Sidebar

Download
PDF
Statistic
Read Counter : 631 Download : 414

Downloads

Download data is not yet available.

Main Article Content

Abstract

Identification of road profiles is needed to provide the input of automotive simulation and endurance testing. The analysis with estimation methods is mostly done to identify road profiles. The main goal of analysis methods is to obtain the data of vertical displacements due to road profile measurement. The acceleration data is obtained from measuring road profile by using 4 sensors of accelerometer placed on each car wheel.  The measuring data is converted to be vertical displacement data by using a "double integrator", however, it is not easy to get accurate results since the signal obtained carries a lot of noise and it is necessary to design the right filter reduce the noise. In this study, the signal filtering methods reducing the noise were used Fast Fourier Transform (FFT) and Kalman Filter (KF) combination. Experiments were carried out by combining Fast Fourier Transform and Kalman Filters using an input signal with unit (volt) in the time domain. In addition, this research focused on preparing the survey data that has been obtained by eliminating the noise to convert becoming the displacement input data for providing the loads of automotive simulation testing.

Keywords

Road profiles Acceleration data Fast Fourier Transform Kalman Filter

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. E. Šabanoviˇc, V. Žuraulis, O. Prentkovskis, and Viktor Skrickij, “Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation.” Sensors 2020, doi: ; doi:10.3390/s20030612.
  2. P. Johannesson, K. Podgórski, and I. Rychlik, “Modelling roughness of road profiles on parallel tracks using roughness indicators,” International Journal of Vehicle Design, vol. 70, no. 2, pp. 183–210, 2016, doi: 10.1504/IJVD.2016.074421.
  3. T. Rateke and A. von Wangenheim, “Road surface detection and differentiation considering surface damages,” Autonomous Robots, vol. 45, no. 2, pp. 299–312, 2021, doi: 10.1007/s10514-020-09964-3.
  4. A. Arunika, J. F. Fatriansyah, and V. A. Ramadheena, “Detection of Asphalt Pavement Segregation Using Machine Learning Linear and Quadratic Discriminant Analyses,” Evergreen, vol. 9, no. 1, pp. 213–218, 2022, doi: 10.5109/4774236.
  5. P. Marcelino, M. de Lurdes Antunes, E. Fortunato, and M. C. Gomes, “Machine learning approach for pavement performance prediction,” International Journal of Pavement Engineering, vol. 22, no. 3, pp. 341–354, 2021, doi: 10.1080/10298436.2019.1609673.
  6. K. Zang, J. Shen, H. Huang, M. Wan, and J. Shi, “Assessing and mapping of road surface roughness based on GPS and accelerometer sensors on bicycle-mounted smartphones,” Sensors (Switzerland), vol. 18, no. 3, 2018, doi: 10.3390/s18030914.
  7. P. Múčka, “Vibration Dose Value in Passenger Car and Road Roughness,” Journal of Transportation Engineering, Part B: Pavements, vol. 146, no. 4, 2020, doi: 10.1061/jpeodx.0000200.
  8. E. Kurakina, S. Evtiukov, and J. Rajczyk, “Forecasting of road accident in the DVRE system,” Transportation Research Procedia, vol. 36, no. SPbOTSIC, pp. 380–385, 2018, doi: 10.1016/j.trpro.2018.12.111.
  9. O. G. Dela Cruz, C. A. Mendoza, and K. D. Lopez, “International Roughness Index as Road Performance Indicator: A Literature Review,” IOP Conference Series: Earth and Environmental Science, vol. 822, no. 1, 2021, doi: 10.1088/1755-1315/822/1/012016.
  10. E. Ranyal, A. Sadhu, and K. Jain, “Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review,” Sensors, vol. 22, 2022, doi: https://doi.org/10.3390/s22083044.
  11. S. Eshkabilov, “Measuring and Assessing Road Profile by Employing Accelerometers and IRI Assessment Tools,” American Journal of Traffic and Transportation Engineering, vol. 3, no. 2, p. 24, 2018, doi: 10.11648/j.ajtte.20180302.12.
  12. F. Rezaei, “Laser Doppler vibrometer and accelerometer for vibrational analysis of the automotive components during Simulink simulation for validation,” pp. 1–18.
  13. A. A. Youssef, N. Al-Subaie, N. El-Sheimy, and M. Elhabiby, “Accelerometer-based wheel odometer for kinematics determination,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–32, 2021, doi: 10.3390/s21041327.
  14. P. Gupta, B. Singh, and Y. Shrivastava, “Robust Techniques for Signal Processing: A Comparative Study,” Evergreen, vol. 9, no. 2, pp. 404–411, 2022, doi: 10.5109/4794165.
  15. S. M. S Rocha, J. S. Flávio Feiteira, P. S. N Mendes, U. P. B Da Silva, and R. F. Pereira, “Method to Measure Displacement and Velocityfrom Acceleration Signals,” Journal of Engineering Research and Application www.ijera.com, vol. 6, no. 6, pp. 52–59, 2016.
  16. V. N. Stavrou, I. G. Tsoulos, and N. E. Mastorakis, “Transformations for fir and iir filters’ design,” Symmetry, vol. 13, no. 4, pp. 1–9, 2021, doi: 10.3390/sym13040533.
  17. F. Naseri, Z. Kazemi, E. Farjah, and T. Ghanbari, “Fast Detection and Compensation of Current Transformer Saturation Using Extended Kalman Filter,” IEEE Transactions on Power Delivery, vol. 34, no. 3, pp. 1087–1097, 2019, doi: 10.1109/TPWRD.2019.2895802.
  18. A. K. Singh and B. C. Pal, “Rate of Change of Frequency Estimation for Power Systems Using Interpolated DFT and Kalman Filter,” IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 2509–2517, 2019, doi: 10.1109/TPWRS.2018.2881151.
  19. J. Zhang, Y. Liu, H. Liu, and J. Wang, “Learning local–global multiple correlation filters for robust visual tracking with kalman filter redetection,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–20, 2021, doi: 10.3390/s21041129.
  20. P. Múčka, “Simulated road profiles according to ISO 8608 in vibration analysis,” Journal of Testing and Evaluation, vol. 46, no. 1, pp. 405–418, 2018, doi: 10.1520/JTE20160265.
  21. M. Arbabpour Bidgoli, A. Golroo, H. Sheikhzadeh Nadjar, A. Ghelmani Rashidabad, and M. R. Ganji, “Road roughness measurement using a cost-effective sensor-based monitoring system,” Automation in Construction, vol. 104, no. May 2018, pp. 140–152, 2019, doi: 10.1016/j.autcon.2019.04.007.
  22. M. M. Chaabane, D. Ben Hassen, M. S. Abbes, S. C. Baslamisli, F. Chaari, and M. Haddar, “Road profile identification using estimation techniques: Comparison between independent component analysis and Kalman filter,” Journal of Theoretical and Applied Mechanics (Poland), vol. 57, no. 2, pp. 397–409, 2019, doi: 10.15632/jtam-pl/104592.
  23. S. Sattar, S. Li, and M. Chapman, “Developing a near real-time road surface anomaly detection approach for road surface monitoring,” Measurement: Journal of the International Measurement Confederation, vol. 185, no. August, p. 109990, 2021, doi: 10.1016/j.measurement.2021.109990.
  24. P. Behera, A. Siddique, T. S. Delwar, M. R. Biswal, Y. Choi, and J. Y. Ryu, “A Novel 65 nm Active-Inductor-Based VCO with Improved Q-Factor for 24 GHz Automotive Radar Applications,” Sensors, vol. 22, no. 13, pp. 1–18, 2022, doi: 10.3390/s22134701.
  25. J. Keenahan, Y. Ren, and E. J. OBrien, “Determination of road profile using multiple passing vehicle measurements,” Structure and Infrastructure Engineering, vol. 16, no. 9, pp. 1262–1275, 2020, doi: 10.1080/15732479.2019.1703757.
  26. B. Zhao, T. Nagayama, and K. Xue, “Road profile estimation, and its numerical and experimental validation, by smartphone measurement of the dynamic responses of an ordinary vehicle,” Journal of Sound and Vibration, vol. 457, pp. 92–117, 2019, doi: 10.1016/j.jsv.2019.05.015.
  27. J. Menegazzo and A. Von Wangenheim, “Multi-Contextual and Multi-Aspect Analysis for Road Surface Type Classification through Inertial Sensors and Deep Learning,” Brazilian Symposium on Computing System Engineering, SBESC, vol. 2020-Novem, 2020, doi: 10.1109/SBESC51047.2020.9277846.
  28. X. Xiao, Z. Sun, and W. Shen, “A Kalman filter algorithm for identifying track irregularities of railway bridges using vehicle dynamic responses,” Mechanical Systems and Signal Processing, vol. 138, p. 106582, 2020, doi: 10.1016/j.ymssp.2019.106582.
  29. F. Liu, S. Gao, and S. Chang, “Displacement estimation from measured acceleration for fixed offshore structures,” Applied Ocean Research, vol. 113, no. January, p. 102741, 2021, doi: 10.1016/j.apor.2021.102741.
  30. Y. Yang, Y. Zhao, and D. Kang, “Integration on acceleration signals by adjusting with envelopes,” Journal of Measurements in Engineering, vol. 4, no. 2, pp. 117–121, 2016.
  31. G. Guo, H. Wang, and D. Bell, “Data reduction and noise filtering for predicting times series,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2419, no. June 2014, pp. 421–429, 2002, doi: 10.1007/3-540-45703-8_39.
  32. S. Pourzeynali, X. Zhu, A. G. Zadeh, M. Rashidi, and B. Samali, “Comprehensive study of moving load identification on bridge structures using the explicit form of newmark-β method: Numerical and experimental studies,” Remote Sensing, vol. 13, no. 12. 2021, doi: 10.3390/rs13122291.
  33. M. Mastriani, “Quantum-Classical Algorithm for an Instantaneous Spectral Analysis of Signals: A Complement to Fourier Theory,” Journal of Quantum Information Science, vol. 08, no. 02, pp. 52–77, 2018, doi: 10.4236/jqis.2018.82005.
  34. M. T. Heideman, D. H. Johnson, and C. S. Burrus, “Gauss and the History of the Fast Fourier Transform,” IEEE ASSP Magazine, vol. 1, no. 4, pp. 14–21, 1984, doi: 10.1109/MASSP.1984.1162257.
  35. J. W. Cooley, P. A. W. Lewis, and P. D. Welch, The Fast Fourier Transform and its Applications, vol. 12, no. 1. 1969.
  36. A. González, E. J. O’Brien, Y. Y. Li, and K. Cashell, “The use of vehicle acceleration measurements to estimate road roughness,” Vehicle System Dynamics, vol. 46, no. 6, pp. 483–499, 2008, doi: 10.1080/00423110701485050.
  37. M. Haddar, S. C. Baslamisli, R. Chaari, F. Chaari, and M. Haddar, “Road profile identification with an algebraic estimator,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 233, no. 4, pp. 1139–1155, 2019, doi: 10.1177/0954406218767470.
  38. F. Tusell, “Kalman Filtering in R,” Journal of Statistical Software, vol. 1, no. 1, pp. 128–129, 2009, doi: 10.1002/wics.10.
  39. Q. Li, R. Li, K. Ji, and W. Dai, “Kalman filter and its application,” Proceedings - 8th International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2015, no. 10, pp. 74–77, 2016, doi: 10.1109/ICINIS.2015.35.
  40. R. Chen and J. S. Liu, “Mixture Kalman ®lters,” J.R. Statist. Soc.B, pp. 493–508, 2000.