Journal of Machine and Computing


Fault Classification in Vehicle Power Transmission using Machine Learning



Journal of Machine and Computing

Received On : 20 January 2022

Revised On : 16 March 2022

Accepted On : 04 May 2022

Published On : 05 July 2022

Volume 02, Issue 03

Pages : 098-102


Abstract


The work describes the application of machine learning (ML) to the categorization and diagnosis of vehicle faults in the power transmission system. For each failure characteristic condition, a machine learning algorithm may be employed to categorize their separate diagnostic elements. The use of acoustic sensors can be used to create a real-time detection approach for vehicle engines and transmission systems. Previously, it was contacting drivers or obscuring services based on vehicle maintenance and driving safety degree under internet of vehicle (IoV) needs. The car's variable acoustic signals are captured and categorised utilising fuzzy logic controllers through the data acquisition device (DAQ) in this manner (FLC). Further, the results are optimized using Particle Swarm Optimization (PSO) technique. While previous systems used 15 fault conditions, we have just used 8 conditions to obtain results


Keywords


Machine learning, Transmission system, Particle swarm optimization, Fuzzy logic.


  1. F.J. D’Amato and D.E. Viasallo, “Fuzzy Control for Active Transmissions,” Journal of Mechatronics, Vol.10, pp. 897- 920, 2020.
  2. D.Hrovart, “Application of optimal control to Dynamic advanced automotive transmission design,” Transactions of ACME, Journal of Dynamic System, Measurement and Control, 115, pp. 328-342, 2020.
  3. Alleyne, A. and J.K. Hedrick “Nonlinear Adaptive Control of Active Transmissions”, IEEE Trans. Contr. Syst. Technol., Vol. 3, pp. 94-101,2020.
  4. Esmailzadeh, E. and H.D. Taghirad: “Active Vehicle Transmissions with Optimal State Feedback Control,” J. Mech. Sci., pp. 1-18. 2020.
  5. Lin, J.S. and I. Kanellakopoulos, “Nonlinear Design of Active Transmission,” IEEE Contr. Syst. Mag., Vol. 17, p. 45-59., 2020
  6. M.V.C. Rao and V. Prahalad: “A tunable fuzzy logic controller for vehicle-active transmission systems,” Elsevier, Fuzzy sets and systems 85(1): pp. 11-21, 2020.
  7. T. Yoshimura, A. Kume, M. Kurimoto and J. Hino: “Construction of an Active transmission system of a Quarter car model using the concept of Sliding mode control,” Journal of Sound and Vibration 239(2), pp. 187-199, 2020
  8. Yahaya Md. Sam, Johari H.S. Osman and M.R.A. Ghani, “A class of proportional –Integral sliding mode control with application to Active transmission,” Elsevier, Systems & control letters, 51, pp. 217-223, 2020
  9. Shiuh-Jer Huang and Wei-Cheng Lin, “Adaptive Fuzzy Controller with Sliding Surface for Vehicle Transmission Control.,” IEEE transactions on fuzzy systems, 11(4), pp. 550-559, 2020.
  10. Eberhart, R. C., and Shi, Y. Particle swarm optimization: developments, applications and resources. Proc. Congress on Evolutionary, Seoul, Korea. Piscataway, NJ: IEEE Service Center. pp. 81-86, 2020.
  11. Y. Lei, F. Jia, J. Lin, S. Xing, and S. X. Ding, “An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data,” IEEE Trans. Industr. Electron., vol. 63, no. 5, pp. 3137-3147, May. 2016.
  12. J. D. Wu, and M. R. Bai “DSP implementation of active noise control in engine exhaust system,” J. J. Appl. Phys., vol. 39, pp. 4982-4986, Aug. 2000.
  13. R. T, H. Ahmadi, F. Sassani, and G. Dumont, “Informative wavelet algorithm in diesel engine diagnosis,” Proceedings of the 2002 IEEE International Symposium on Intelligent Control, Vancouver, Canada., Dec. 361-366, 2002.
  14. J. Lin, “Feature extraction of machine sound using wavelet and its application in fault diagnosis,” NDT&E International., vol. 34. no. 1, pp. 25-30, Jan. 2001.
  15. M. H. Weatherspoon, and D. Langoni, “Accurate and efficient modeling of FET cold noise sources using ANNs,” IEEE. Trans., vol. 57, no. 2, pp. 432-437, Jan. 2008.
  16. H. Anandakumar and K. Umamaheswari, “Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers,” Cluster Computing, vol. 20, no. 2, pp. 1505–1515, Mar. 2017.
  17. H. Anandakumar and K. Umamaheswari, “A bio-inspired swarm intelligence technique for social aware cognitive radio handovers,” Computers & Electrical Engineering, vol. 71, pp. 925–937, Oct. 2018. doi:10.1016/j.compeleceng.2017.09.016
  18. R. Muralishankar, and A. Sangwan, “Theoretical complex cepstrum of DCT and warped DCT filters,” IEEE. Sign. Process., vol. 14, no. 5,pp. 367-370, May. 2007.
  19. H. K. Kim, and R. C. Rose, “Cepstrum-domain model combination based on decomposition of speech and noise using MMSE-LSA for ASR in noisy environments,” IEEE. Trans. Audio Speech, vol. 17 no. 4, pp. 704-713, May. 2009.
  20. L. Shi, I. Ahmad, Y. He, and K. Chang, “Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments,” J. Commun. Net., vol. 20, no. 5, pp. 509-518, Oct. 2018.

Acknowledgements


Authors thanks to Department of Computer Science and Engineering for this research support.


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Cite this article


Suriya M, Raakesh R, Srikaran R S, Surya K, Vino, “Fault Classification in Vehicle Power Transmission using Machine Learning”, Journal of Machine and Computing, vol.2, no.3, pp. 098-0102, July 2022. doi: 10.53759/7669/jmc202202014.


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© 2022 Suriya M, Raakesh R, Srikaran R S, Surya K, Vino. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.