Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Machine Fault Diagnosis Method based on DWT Power Spectral Density using Multi Patten Recognition

다중 패턴 인식 기법을 이용한 DWT 전력 스펙트럼 밀도 기반 기계 고장 진단 기법

  • Kang, Kyung-Won (Dept. of Information and Communications Eng., Tongmyong University) ;
  • Lee, Kyeong-Min (College of General Education, Tongmyong University) ;
  • Vununu, Caleb (Dept. of IT Convergence and Application Eng., Pukyong National University) ;
  • Kwon, Ki-Ryong (Dept. of IT Convergence and Application Eng., Pukyong National University)
  • Received : 2019.07.03
  • Accepted : 2019.10.23
  • Published : 2019.11.30
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Abstract

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

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References

  1. G. Byrne, D. Dornfeld, I. Inasaki, G. ketterler, W. Konig, and R. Teti, "Tool Condition Monitoring(TCM)-The Status of Research and Industrial Application," International Institution for Production Engineering Research Annals, Vol. 44, Issue 2, pp. 541-567, 1995.
  2. R. Teti, K. Jemielniak, G. O'Donnell, and D. Dornfeld, "Advanced Monitoring of Machining Operation," International Institution for Production Engineering Research Annals, Vol. 59, Issue 2, pp. 717-739, 2010.
  3. M.M. Polycarpou and A.T. Vemuri, "Learning Methodology for Failure Detection and Accommodation," IEEE Control Systems Magazine, Vol. 15, Issue 3, pp. 16-24, 1995. https://doi.org/10.1109/37.387613
  4. K. Kumamaru, J. Hu, K. Inoue, and T. Soderstrom, "Robust Fault Detection Using Index of Kullback Discrimination Information," International Federation of Automatic Control, Vol. 29, Issue 1, pp. 6524-6529, 1996.
  5. S. Takata, J.H. Ahn, M. Miki, Y. Miyao, and T. Sata, "A Sound Monitoring System for Fault Detection of Machine and Machining States," International Institution for Production Engineering Research Annals, Vol. 35, Issue 1, pp. 289-292, 1986.
  6. Y. Maki and K.A. Loparo, "A Neural-network Approach to Fault Detection and Diagnosis in Industrial Processes," IEEE Transactions on Control Systems Technology, Vol. 5, Issue 6, pp. 529-541, 1997. https://doi.org/10.1109/87.641399
  7. C. Chen, B. Zhang, G. Vachtsevanos, and M. Orchard, "Machine Condition Prediction Based on Adaptive Neuro-fuzzy and High-order Particle Filtering," IEEE Transactions on Industrial Electronics, Vol. 58, Issue 9, pp. 4353-4364, 2011. https://doi.org/10.1109/TIE.2010.2098369
  8. Y.S. Wang, Q.H. Ma, Q. Zhu, and L. Zhao, "An Intelligent Approach for Engine Fault Diagnosis Based on Hibert-huang Transform and Support Vector Machine," Applied Acoustics, Vol. 75, pp. 1-9, 2014. https://doi.org/10.1016/j.apacoust.2013.07.001
  9. 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
  10. H. Ocak and K.A. Loparo, "Estimation of the Running Speed and Bearing Defect Frequency of an Induction Motor from Vibration Data," Mechanical Systems and Signal Processing, Vol. 18, Issue 3, pp. 515-533, 2004. https://doi.org/10.1016/S0888-3270(03)00052-9
  11. K.M. Lee, C. Vununu, K.S. Moon, S.H. Lee, and K.R. Kwon, “Automatic Machine Fault Diagnosis System Using Discrete Wavelet Transform and Machine Learning,” Journal of Korea Multimedia Society, Vol. 20, No. 8, pp. 1299-1311, 2017. https://doi.org/10.9717/kmms.2017.20.8.1299