Tribology Letters

, Volume 47, Issue 1, pp 1–15 | Cite as

A New Intelligent Fusion Method of Multi-Dimensional Sensors and Its Application to Tribo-System Fault Diagnosis of Marine Diesel Engines

  • Zhixiong Li
  • Xinping Yan
  • Zhiwei Guo
  • Peng Liu
  • Chengqing Yuan
  • Zhongxiao Peng
Original Paper


Marine diesel engines, a critical component to provide power for entire ships, have been received and still need considerable attentions to ensure their safety operation. Vibration and wear debris analysis are currently the most popular techniques for diesel engine condition monitoring and fault diagnosis. However, they are usually used independently in practice, and limited work has been done to address the integration of data collected using the two techniques. To enhance early fault detections, a new fault diagnosis technique for the marine diesel engine has been proposed by the information fusion of the vibration and wear particle analyses in this paper. A new independent component analysis with reference algorithm (ICA-R) using the empirical mode decomposition based reference extraction scheme was adopted to identify the characteristic source signals of the engine vibration collected from multi-channel sensors. The advantage of this approach performed at a data fusion level is that the ICA-R can extract only the relevant source directly related to the engine fault features in one separation cycle via incorporating prior knowledge. The statistical values of the recovered source signals were then calculated. The above vibration features, along with the wear particle characteristics, were used as the feature vectors for the engine fault detection. Lastly, the improved simplified fuzzy ARTMAP (SFAM) was applied to integrate the distinctive features extracted from the two techniques at a decision level to detect faults in a supervised learning manner. Particularly, the immune particle swarm optimization was used to tune the vigilance parameter of the SFAM to improve the identification performance. The experimental tests were implemented on a diesel engine set-up to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have shown that distinguished fault features can be extracted and the fault identification accuracy is satisfactory. Moreover, the fault detection rate of the integration approach has been enhanced by 16.0 % or better when compared with using the two techniques separately.


Marine diesel engine Tribo-system Fault diagnosis Vibration analysis Wear debris analysis 



This project is sponsored by the grants from the State Key Program of National Natural Science of China (NSFC) (No. 51139005), the National Natural Sciences Foundation of China (NSFC) (No. 50975213), and the Program of Introducing Talents of Discipline to Universities (No. B08031).


  1. 1.
    The Swedish club highlights, 3rd edn. The Swedish Club, Sweden (2005)Google Scholar
  2. 2.
    Jones, N.B., Li, Y.H.: A review of condition monitoring and fault diagnosis for diesel engines. TriboTest 6(3), 267–291 (2000)CrossRefGoogle Scholar
  3. 3.
    Yan, X.P., Zhao, C.H., Lu, Z.Y., Zhou, X.C., Xiao, H.L.: A study of information technology used in oil monitoring. Tribol. Int. 38(10), 879–886 (2005)CrossRefGoogle Scholar
  4. 4.
    Lamaris, V.T., Hountalas, D.T.: A general purpose diagnostic technique for marine diesel engines—application on the main propulsion and auxiliary diesel units of a marine vessel. Energy Convers. Manage. 51(4), 740–753 (2010)CrossRefGoogle Scholar
  5. 5.
    Yang, J., Pu, L., Wang, Z., Zhou, Y., Yan, X.: Fault detection in a diesel engine by analysing the instantaneous angular speed. Mech. Syst. Signal Process. 15(3), 549–564 (2001)CrossRefGoogle Scholar
  6. 6.
    Al-Qattan, M.J., Al-Juwayhel, F., Ball, A., Elhaj, M., Gu, F.: Instantaneous angular speed and power for the diagnosis of single-stage, double-acting reciprocating compressor. Proc. IMechE Part J: J. Eng. Tribol. 223(1), 95–114 (2009)CrossRefGoogle Scholar
  7. 7.
    Peng, Z., Kessissoglou, N.J., Cox, M.: A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques. Wear 258(11–12), 1651–1662 (2005)CrossRefGoogle Scholar
  8. 8.
    Peng, Z., Kessissoglou, N.: An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear 255(7–12), 1221–1232 (2003)CrossRefGoogle Scholar
  9. 9.
    Zhang, D.D.: Bearing fault diagnosis based on the dimension-temporal information. Proc. IMechE Part J: J. Eng. Tribol. 225(8), 806–813 (2011)CrossRefGoogle Scholar
  10. 10.
    Chee, T., Phil, I., David, M.: A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears. Mech. Syst. Signal Process. 21(1), 208–233 (2007)CrossRefGoogle Scholar
  11. 11.
    Mathew, J., Stecki, J.S.: Comparison of vibration and direct reading ferrographic techniques in application to high-speed gears operating under steady and varying load conditions. Lubr. Eng. 43(8), 646–653 (1987)Google Scholar
  12. 12.
    Maxwell, H., Johnson, B.: Vibration and lube oil analysis in an integrated predictive maintenance program. In: Proceedings of 21st Annual Meeting of the Vibration Institute, USA, pp. 117–124 (1997)Google Scholar
  13. 13.
    Akagaki, T., Nakamur, M., Monzen, T., Kawabata, M.: Analysis of the behaviour of rolling bearings in contaminated oil using some condition monitoring techniques. Proc. IMechE Part J: J. Eng. Tribol. 220(5), 447–453 (2006)CrossRefGoogle Scholar
  14. 14.
    Maru, M.M., Castillo, R.S., Padovese, L.R.: Study of solid contamination in ball bearings through vibration and wear analyses. Tribol. Int. 40(3), 433–440 (2007)CrossRefGoogle Scholar
  15. 15.
    Li, Z., Yan, X., Yuan, C., Peng, Z., Li, L.: Virtual prototype and experimental research gear multi—fault diagnosis using wavelet—autoregressive model and principal component analysis method. Mech. Syst. Signal Process. 25(7), 2589–2607 (2011)CrossRefGoogle Scholar
  16. 16.
    Cao, C., Yang, S., Yang, J.: A new fault diagnosis method for a rotor of a steam turbine generator set based on instantaneous energy distribution characteristics. J. Vib. Shock 28(3), 35–39 (2009)Google Scholar
  17. 17.
    Li, Z., Yan, X.: Independent component analysis and manifold learning with applications to fault diagnosis of VSC-HVDC systems. Hsi-An Chiao Tung Ta Hsueh 45(1), 44–48 (2011)Google Scholar
  18. 18.
    Li, Z., Yan, X., Yuan, C., Zhao, J., Peng, Z.: A new method of nonlinear feature extraction for multi-fault diagnosis of rotor systems. Noise Vib. Worldwide 41(10), 29–37 (2010)CrossRefGoogle Scholar
  19. 19.
    Li, W., Gu, F., Ball, A., Leung, A., Phipps, C.: A study of the noise from diesel engines using the independent component analysis. Mech. Syst. Signal Process. 15(6), 1165–1184 (2001)CrossRefGoogle Scholar
  20. 20.
    Ge, N., Liu, Y.: Application of independent component analysis to decomposition of engine acoustic signals. Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban) 39(4), 454–457 (2006)Google Scholar
  21. 21.
    Xu, H., Hao, Z., Guo, L., Jing, G.: Noise source identification of internal combustion engine based on independent component and wavelet analysis. Neiranji Gongcheng 28(6), 61–65 (2007)Google Scholar
  22. 22.
    Hao, Z., Jin, Y., Yang, C.: Study of engine noise based on independent component analysis. J. Zhejiang Univ. Sci. A 8(5), 772–777 (2007)CrossRefGoogle Scholar
  23. 23.
    Gao, H., Ouyang, G., Zhu, S., Shao, L.: Preprocessing vibration signals on cylinder head of diesel engine before blind separation based on SVD. Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban) 32(4), 750–752 + 756 (2008)Google Scholar
  24. 24.
    Lu, W., Rajapakse, J.C.: ICA with reference. Neurocomputing 69(16–18), 2244–2257 (2006)CrossRefGoogle Scholar
  25. 25.
    Yu, G., Liang, X., Wang, J.: Fault feature separation for fault diagnosis of rotating machinery using ICA with reference. In: Proceedings of the 9th International Conference on Reliability, Maintainability and Safety, IEEE Computer Society, United States, pp. 1010–1014 (2011)Google Scholar
  26. 26.
    Li, N., Chen, M., Fang, Y., Li, H.: Fault diagnose of rotating system based on ICA with reference and RBF networks. In: Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, Institute of Electrical and Electronics Engineers Computer Society, United States, pp. 174–178 (2006)Google Scholar
  27. 27.
    Hyvärinen, A.: Survey on independent component analysis. Neural Computing Surveys 2, 94–128 (1999)Google Scholar
  28. 28.
    Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)CrossRefGoogle Scholar
  29. 29.
    Li, C., Liao, G., Shen, Y.: An improved method for independent component analysis with reference. Digital Signal Process. Rev. J. 20(2), 575–580 (2010)CrossRefGoogle Scholar
  30. 30.
    Grossberg, S.: Adaptive pattern classification and universal recoding, II: feedback, expectation, olfaction and illusions. Biol. Cybern. 23(3), 187–202 (1976)CrossRefGoogle Scholar
  31. 31.
    Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy ARTMAP: neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netw. 3(5), 698–713 (1992)CrossRefGoogle Scholar
  32. 32.
    Vakil-Baghmisheh, M.T., Pavešic, N.: A fast simplified Fuzzy ARTMAP network. Neural Process. Lett. 17(3), 273–316 (2003)CrossRefGoogle Scholar
  33. 33.
    Kasuba, T.: Simplified fuzzy ARTMAP. AI Expert 8(11), 18–25 (1993)Google Scholar
  34. 34.
    Rajasekaran, S., Vijayalakshmi, G.: Simplified fuzzy ARTMAP as pattern recognizer. J. Comput. Civ. Eng. 14(2), 92–99 (2000)CrossRefGoogle Scholar
  35. 35.
    Amis, G., Carpenter, G.: Self-supervised ARTMAP. Technical Report CAS/CNS TR-2009-006, Boston University, Boston, MA (2009)Google Scholar
  36. 36.
    Chen, Z., Yan, Z., Liu, S.: Classification of breast cancer genes with a simplified Fuzzy ARTMAP approach. J. Shanghai Univ. (Nat. Sci. Ed.) 12(4), 354–358 (2006)Google Scholar
  37. 37.
    Palaniappan, R., Ravi, K.V.R.: Improving visual evoked potential feature classification for person recognition using PCA and normalization. Pattern Recogn. Lett. 27(7), 726–733 (2006)CrossRefGoogle Scholar
  38. 38.
    Li, A., Wang, L., Li, J., Ji, C.: Application of immune algorithm-based particle swarm optimization for optimized load distribution among cascade hydropower stations. Comput. Math. Appl. 57(11–12), 1785–1791 (2009)CrossRefGoogle Scholar
  39. 39.
    Yan, X., Yuan, C., Liu, Z., Zong, C., Bai, X.: Study of simulation tester for key rubbing pairs in internal-combustion engine. Adv. Mater. Res. 97–101, 4359–4362 (2010)CrossRefGoogle Scholar
  40. 40.
    Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 454, 903–905 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Zhixiong Li
    • 1
  • Xinping Yan
    • 1
  • Zhiwei Guo
    • 1
  • Peng Liu
    • 1
  • Chengqing Yuan
    • 1
  • Zhongxiao Peng
    • 2
  1. 1.Reliability Engineering Institute, School of Energy and Power Engineering, Key Laboratory of Marine Power Engineering and Technology, Ministry of TransportationWuhan University of TechnologyWuhanChina
  2. 2.School of Mechanical & Manufacturing EngineeringThe University of New South WalesSydneyAustralia

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