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access icon free Acoustic signal based detection and localisation of faults in motorcycles

© The Institution of Engineering and Technology
Received 08/12/2012, Accepted 04/07/2013, Revised 05/06/2013, Published 27/08/2013

Vehicles produce dissimilar sound patterns under different working conditions. The study approaches detection and localisation of faults in motorcycles, by exploiting the variations in the spectral behaviour. Fault detection stage uses chaincode of the pseudospectrum of the sound signal. Fault localisation stage uses statistical features derived from the wavelet subbands. Dynamic time warping classifier is used for classification of samples into healthy and faulty in the first stage. In essence, the same classifier classifies the faulty samples into valve-setting, muffler leakage and timing chain faults in the second stage. Classification results are over 90% for both the stages. The proposed study finds applications in surveillance, fault diagnosis of vehicles, machinery, musical instruments etc.

Inspec keywords: acoustic signal detection; mechanical engineering computing; valves; exhaust systems; feature extraction; statistical analysis; fault diagnosis; spectral analysis; motorcycles; signal classification; silencers

Other keywords: sound signal pseudospectrum chaincode; timing chain faults; working conditions; acoustic signal based motorcycle fault localisation; musical instrument fault diagnosis; machinery fault diagnosis; muffler leakage; spectral behaviour; statistical features; acoustic signal based motorcycle fault detection; vehicle fault diagnosis; sound patterns; dynamic time warping classifier; fault detection stage; valve-setting; wavelet subbands

Subjects: Civil and mechanical engineering computing; Digital signal processing; Acoustic properties (mechanical engineering); Acoustic signal processing; Other topics in statistics; Signal detection; Statistics; Engines; Vehicle mechanics; Maintenance and reliability; Other topics in statistics; Probability theory, stochastic processes, and statistics; Mechanical engineering applications of IT

References

    1. 1)
      • 17. Guo, B., Nixon, M.S., Damarla, T.: ‘Improving acoustic vehicle classification by information fusion’ (Springer Verlag, Pattern Analysis and Applications, 2012), vol. 15, no. 1, pp. 29–43.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
      • 22. www.physics.sfsu.edu/~dcuneo/radar-mount/music.pdf (for a tutorial on ‘The linear sampling method and the MUSIC algorithm’, by Margaret Cheney, 2003).
    13. 13)
      • 1. http://www.moneylife.in/article/car-sales-in-india-likely-to-expand-3-5-in-fy14-siam/32142.html [SIAM: Society of Indian Automobile Manufacturers, Predictions for two-wheeler sales in Financial year 2013–14]..
    14. 14)
      • 28. Furtună, T.F.: ‘Dynamic programming algorithms in speech recognition’, Rev. Inf. Econ. nr., 2, (46), 2008, pp. 94–98.
    15. 15)
      • 17. Guo, B., Nixon, M.S., Damarla, T.: ‘Improving acoustic vehicle classification by information fusion’ (Springer Verlag, Pattern Analysis and Applications, 2012), vol. 15, no. 1, pp. 29–43.
    16. 16)
      • 29. http://www.mqld.org.au/fileadmin/user_upload/state_files/qld/Documents/Forms/Pg120_Sound_Basics.pdf (For recording environment setup).
    17. 17)
      • 11. Datta, A., Mavroidis, C., Krishnasamy, J., Hosek, M.: ‘Neural network based fault diagnostics of industrial robots using wavelet multi-resolution analysis’. Proc. American Control Conf., New York City, USA, 2007, pp. 1858–1863.
    18. 18)
      • 18. Madain, M., Al-Mosaiden, A., Al-khassaweneh, M.: ‘Fault diagnosis in vehicle engines using sound recognition techniques’. Proc. IEEE Int. Conf. Electro/Information Technology (EIT), Illinois State University, Normal, IL, USA, 20–22 May 2010, pp. 1–4.
    19. 19)
      • 3. Sazali, P.M.P., Zubir, Y.M., Zin, M.: ‘Motorbike engine faults diagnosing system using entropy and functional link neural network in wavelet domain’. Proc. Int. Conf. Man–Machine Systems, Penang, Malaysia, 11–13 October 2009, pp. 2B4-1–2B4-5.
    20. 20)
      • 23. Pereira, L.A., Fernandes, D., Gazzana, D.S., Libano, F.B., Haffner, S.: ‘Application of the Welch, Burg and MUSIC Methods to the detection of rotor cage faults of induction motors’. IEEE/PES Transmission andDistribution Conf. Exposition, Caracas, Latin America, 15–18 August 2006, TDC ‘06. pp. 1–6.
    21. 21)
      • 8. Ayoubi, M.: ‘Fuzzy systems design based on a hybrid neural structure and application to the fault diagnosis of technical processes’, J. Control Eng. Pract., 1996, 4, (1), pp. 35–42 (doi: 10.1016/0967-0661(95)00204-8).
    22. 22)
      • 13. Lin, J.: ‘Feature extraction of machine sound using wavelet and its application in fault diagnosis’, J. NDT&E Int., 2001, 34, pp. 25–30 (doi: 10.1016/S0963-8695(00)00025-6).
    23. 23)
      • 9. He, D., Li, R., Bechhoefer, E.: ‘Split torque type gearbox fault detection using acoustic emission and vibration sensors’. Proc. IEEE Int. Conf. Networking, Sensing and Control, Chicago, USA, 11–13 April 2010, pp. 62–66.
    24. 24)
      • 19. Zhenshan, L., Jianqun, W., Xuejun, R., Guozhong, Y.: ‘Traffic information extraction of vehicle acoustic signal based on wavelet packet analysis’. Proc. 29th Chinese Control Conf. (CCC), Beijing, China, 29–31 July 2010, pp. 5438–5440.
    25. 25)
      • 14. Ganjdanesh, Y., Manjili, Y.S., Vafaei, M., Zamanizadeh, E., Jahanshahii, E.: ‘Fuzzy fault detection and diagnosis under severely noisy conditions using feature-based approaches’. Proc. American Control Conf., Washington, USA, 11–13 June 2008, pp. 3319–3324.
    26. 26)
      • 6. Heidarbeigi, K., Ahmadi, H., Omid, M., Tabatabaeefar, A.: ‘Fault diagnosis of Massey Ferguson gearbox using power spectral density’, J. Agric. Technol., 2009, 5, (1), pp. 1–6.
    27. 27)
      • 15. Sun, H., Wu, T., Zhu, H.-c.: ‘Research on the vehicle fault diagnosis base on the fuzzy dependability algorithm’, Adv. Inf. Sci. Serv. Sci., 2012, 4, (3), pp. 91–96.
    28. 28)
      • 26. Pikrakis, A., Theodoridis, S., Kamarotos, D.: ‘Recognition of isolated musical patterns using context dependent dynamic time warping’, IEEE Trans. Speech Audio Process., 11, (3), 2003, pp. 175–183 (doi: 10.1109/TSA.2003.811533).
    29. 29)
      • 21. Schmidt, R.O.: ‘Multiple emitter location and signal parameter estimation’, IEEE Trans. Antennas Propag., AP-34, (3), 1986, pp. 276–280 (doi: 10.1109/TAP.1986.1143830).
    30. 30)
      • 27. Cowling, M., Sitte, R.: ‘Comparison of techniques for environmental sound recognition’, Pattern Recognit. Lett., 24, 2003, pp. 2895–2907 (doi: 10.1016/S0167-8655(03)00147-8).
    31. 31)
      • 25. Sakoe, H., Chiba, S.: ‘Dynamic programming algorithm optimization for spoken word recognition’, IEEE Trans. Acoustics, Speech Signal Process., 1978, 26, (1), pp. 43–49 (doi: 10.1109/TASSP.1978.1163055).
    32. 32)
      • 24. http://www.conceptualwavelets.com/docs/wavelets_ch1.pdf (for DB4 wavelets).
    33. 33)
      • 4. Wu, J.D., Chang, E.C., Liao, S.Y., Kuo, J.M., Huang, C.K.: ‘Fault classification of a scooter engine platform using wavelet transform and artificial neural network’. Proc. Int. Multi Conf. Engineers and Computer Scientists, vol. 1, Hong Kong, 18–20 March 2009, pp. 58–63.
    34. 34)
      • 5. Lin, J., Zuo, M.J.: ‘Gearbox fault diagnosis using adaptive wavelet filter’, Mech. Syst. Signal Process., 2003, 17, (6), pp. 1259–1269 (doi: 10.1006/mssp.2002.1507).
    35. 35)
      • 12. Luiz, A., Carneiro, G., da Silva, A.A., Upadhyaya, B.R.: ‘Incipient fault detection of motor-operated valves using wavelet transform analysis’, Nucl. Eng. Des., 2008, 238, (9), pp. 2453–2459 (doi: 10.1016/j.nucengdes.2008.03.013).
    36. 36)
      • 22. www.physics.sfsu.edu/~dcuneo/radar-mount/music.pdf (for a tutorial on ‘The linear sampling method and the MUSIC algorithm’, by Margaret Cheney, 2003).
    37. 37)
      • 10. Engin, S.N., Gulez, K.: ‘A wavelet transform–artificial neural networks (WT-ANN) based rotating machinery fault diagnostics methodology’. Proc. IEEE NSIP, Antalya, Turkey, 20–23 June 1999, pp. 714–720.
    38. 38)
      • 2. Anami, B.S., Pagi, V.B., Magi, S.M.: ‘Wavelet based acoustic analysis for determining health condition of two-wheelers’, Elsevier J. Appl. Acoust., 2011, 72, (7), pp. 464–469 (doi: 10.1016/j.apacoust.2011.01.015).
    39. 39)
      • 20. http://www.mathworks.in/help/toolbox/signal/pmusic.html (for MUSIC algorithm).
    40. 40)
      • 16. Bhave, N., Rao, P.: ‘Vehicle engine sound analysis applied to traffic congestion estimation’. Proc. CMMR/FRSM, Utkal University, Bhubaneswar, India, 9–12 March 2011.
    41. 41)
      • 7. JunHong, Z., Bing, H.: ‘Analysis of engine front noise using sound intensity techniques’, Mech. Syst. Signal Process., 2005, 19, pp. 213–221 (doi: 10.1016/j.ymssp.2004.03.007).
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