© The Institution of Engineering and Technology
Service station experts examine the sound patterns of the motorcycles to diagnose the faults. Automatic fault diagnosis is a challenging task and more so is recognition of multiple faults. This study presents a methodology for localisation of multiple faults in motorcycles. The sound signatures of multiple faults are constructed by fusing the individual signatures of faults from engine and exhaust subsystems. Energy distributions in the approximation coefficients of wavelet packets are used as features. Among the classifiers used, artificial neural network is found suitable for detecting the presence of multiple faults. The recognition accuracy is over 78% when trained with individual fault signatures and over 88% when trained with combined fault signatures.
References
-
-
1)
-
20. Maciejewski, H., Mazurkiewicz, J., Skowron, K., Walkowiak, T.: ‘Neural networks for vehicle recognition’. Proc. Sixth Int. Conf. on Microelectronics for Neural Networks. Evolutionary and fuzzy Systems, Dresden, Germany, 1997, pp. 292–296.
-
2)
-
19. Shuxiang, X., Ling, C.: ‘A novel approach for determining the optimal number of hidden layer neurons for FNN's and its application in data mining’. Proc. Fifth Int. Conf. on Information Technology and Applications (ICITA 2008), Cairns, Queensland, Australia, 23–26 June 2008, pp. 683–686.
-
3)
-
14. Junsheng, C., Dejie, Y., Yu, Y.: ‘A fault diagnosis approach for gears based on IMF-AR model and SVM’, EURASIP J. Adv. Signal Process., doi:pi10.1155/2008/647135, 2008.
-
4)
-
22. Choe, H.C., Karlsen, R.E., Gerhart, G.R., Meitzler, T.: ‘Wavelet-based ground vehicle recognition using acoustic signals’. Proc. of SPIE, March 1996, pp. 434–445.
-
5)
-
7. Sunho, L., Seunghan, Y., Bongsob, S.: ‘Hierarchical fault recognition and diagnosis for unmanned ground vehicles’. Proc. Seventh Asian Control Conf. on Hong Kong, China, 27–29 August 2009, pp. 881–886.
-
6)
-
5. Zhu, D., Bai, J., Yang, S.X.: ‘A multi-fault diagnosis method for sensor systems based on principal component analysis’, Sensors 2010, 2009, 10, (1), pp. 241–253.
-
7)
-
2. Zhixiong, L., Xinping, Y., Chengqing, Y., Zhongxiao, P., Li, L.: ‘Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method’, Mech. Syst. Signal Process., 2011, 25, (7), pp. 2589–2607 (doi: 10.1016/j.ymssp.2011.02.017).
-
8)
-
11. Wei, L., Pu, H., Xu, L.: ‘Fault diagnosis for engine based on EMD and wavelet packet BP neural network’. Proc. Third Int. Symp. on Intelligent Information Technology Application, Nanchang, China, 21–22 November 2009, pp. 672–676.
-
9)
-
21. Averbuch, A., Zheludev, V.A., Rabin, N., Schclar, A.: ‘Wavelet-based acoustic recognition of moving vehicles’, Multidimens. Syst. Signal Process., 2009, 20, (1), pp. 1–25 (doi: 10.1007/s11045-008-0058-z).
-
10)
-
3. Anami, B.S., Pagi, V.B.: ‘Integration of pseudospectral segments of sound signals for fault location in motorcycles’, Int. J. Adv. Sci. Technol., 2012, 47, pp. 77–90.
-
11)
-
12)
-
10. 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. MultiConf. of Engineers and Computer Scientists, IMECS 2009, Hong Kong, China, 18–20 March 2009, vol. 1, pp. 58–63.
-
13)
-
6. Ayhan, B., Mo-Yuen, C., Myung-Hyun, S.: ‘Multiple signature processing-based fault recognition schemes for broken rotor bar in induction motors’, IEEE Trans. Energy Convers., 2005, 20, (2), pp. 336–343 (doi: 10.1109/TEC.2004.842393).
-
14)
-
15. PinTan, C., Edwards, C.: ‘Sliding mode observers for detection and reconstruction of sensor faults’. Automatica2002, 38, pp. 1815–1821 (doi: 10.1016/S0005-1098(02)00098-5).
-
15)
-
4. Purushotham, V., Narayanan, S., Prasad, S.: ‘Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition’, NDT E Int., 2005, 38, (8), pp. 654–664 (doi: 10.1016/j.ndteint.2005.04.003).
-
16)
-
17)
-
16. Yu, D.L., Chang, T.K., Yu, D.W., ‘Adaptive neural model-based fault tolerant control for multi-variable processes’, Eng. Appl. Artif. Intell., 2005, 18, pp. 393–411 (doi: 10.1016/j.engappai.2004.10.003).
-
18)
-
18. Nasir, R.: ‘Texture classification using discriminant wavelet packet subbands’. Proc. 45th Midwest Symp. on Circuits and Systems, MWSCAS-2002, 4–7 August 2002, vol. 3, pp. 300–303.
-
19)
-
9. Zhang, J.H., Han, B.: ‘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).
-
20)
-
17. Sakoe, H., Chiba, S.: ‘Dynamic programming algorithm optimization for spoken word recognition’, IEEE Trans. Acoust. Speech Signal Process., 1978, 26, (1), pp. 43–49 (doi: 10.1109/TASSP.1978.1163055).
-
21)
-
13. 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).
-
22)
-
8. 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).
-
23)
-
12. Heidarbeigi, K., Hojat, A., Omid, M., Tabatabaeefar, A.: ‘Fault diagnosis of Massey Ferguson gearbox using power spectral density’, J. Agric. Technol., 2009, 5, (1), pp. 1–6.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2013.0037
Related content
content/journals/10.1049/iet-its.2013.0037
pub_keyword,iet_inspecKeyword,pub_concept
6
6