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Korea Multimedia Society (한국멀티미디어학회)
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Sound Based Machine Fault Diagnosis System Using Pattern Recognition Techniques

  • Vununu, Caleb (Dept. of IT Convergence and Application Engineering, Pukyong National University) ;
  • Moon, Kwang-Seok (Dept. of Electronics Engineering, Pukyong National University) ;
  • Lee, Suk-Hwan (Dept. of Information Security, Tongmyong University) ;
  • Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)
  • Received : 2017.01.03
  • Accepted : 2017.01.17
  • Published : 2017.02.28
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Abstract

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

Keywords

References

  1. M.M. Polycarpou and A.T. Vemuri, "Learning Methodology for Failure Detection and Accommodation," IEEE Control Systems, Vol. 15, Issue 3, pp. 16-24, 1995. https://doi.org/10.1109/37.387613
  2. N.R. Sakthivel, V. Sugumaran, and B.B. Nair, "Application of Support Vector Machine (SVM) and Proximal Support Vector Machine (PSVM) for Fault Classification of Mono Block Centrifugal Pump," International Journal of Data Analysis Techniques and Strategies, Vol. 2, No. 1, pp. 38-61, 2010. https://doi.org/10.1504/IJDATS.2010.030010
  3. N. Tandon and B.C. Nakra, "Vibration and Acoustic Monitoring Techniques for the Detection of Defects in Rolling Element Bearings-A Review," The Shock and Vibration Digest, Vol. 24, No. 3, pp. 3-11, 1992. https://doi.org/10.1177/058310249202400303
  4. P.W. Tse, G.H. Xu, L. Qu, and S.R. Kumara, "An Effective and Portable Electronic Stethoscope for Fault Diagnosis by Analysing Machine Running Sound Directly," International Journal of Acoustics and Vibration, Vol. 6, No. 1, pp. 23-31, 2001.
  5. H. Hotelling, "Analysis of a Complex of Statistical Variables into Principal Components," Journal of Educational Psychology, Vol. 24, Issue 6, pp. 417-441, 1933. https://doi.org/10.1037/h0071325
  6. V. Emamian, M. Kaveh, and A.H. Tewfik, "Robust Clustering of Acoustic Emission Signals Using the Kohonen Network," Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 3891-3894, 2000.
  7. M. Subrahmanyam and C. Sujatha, "Using Neural Networks for the Diagnosis of Localized Defects in Ball Bearings," Tribology International, Vol. 30, No. 10, pp. 739-752, 1997. https://doi.org/10.1016/S0301-679X(97)00056-X
  8. T. Villmann, "Neural Network Approaches in Medicine-A Review of Actual Developments," Proceedings of the European Symposium on Artificial Neural Networks, pp. 165-176, 2000.
  9. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, Wiley, New York, 2001.
  10. Sang-Il Choi, "Construction of Composite Feature Vector Based on Discriminant Analysis for Face Recognition," Journal of Korea Multimedia Society, Vol. 18, Issue 7, pp. 834-842, 2015. https://doi.org/10.9717/kmms.2015.18.7.834
  11. M. Saimurugan and K.I. Ramachandran, "A Comparative Study of Sound and Vibrations Signals in Detection of Rotating Machine Faults Using Support Vector Machine and Independent Component Analysis," International Journal of Data Analysis Techniques and Strategies, Vol. 6, No. 2, pp. 188-204, 2014. https://doi.org/10.1504/IJDATS.2014.062458
  12. M.J. Zurada, Introduction to Artificial Neural Systems, Jaico Publishing House, Delhi, 1999.
  13. D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning Representations by Backpropagating Errors," Nature, Vol. 323, No. 9, pp. 533-536, 1986. https://doi.org/10.1038/323533a0
  14. P.K. Kankar, Satish C. Sharma, and S.P. Harsha, "Fault Diagnosis of Ball Bearings Using Machine Learning Methods," Expert Systems with Applications, Vol. 38, Issue 3, pp. 1876-1886, 2011. https://doi.org/10.1016/j.eswa.2010.07.119

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