Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Fault Diagnosis of Induction Motor using Linear Predictive Coding and Deep Neural Network

LPC와 DNN을 결합한 유도전동기 고장진단

  • Received : 2017.09.26
  • Accepted : 2017.11.15
  • Published : 2017.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.

Keywords

References

  1. S.M. Lee, Fault Diagnosis for Rotating Machinery with Clearance Using HHT, Master's Thesis of Univ. of Sungkyunkwan, 2007.
  2. D.V. Tuan, Fault Detection and Diagnosis for Induction Motors using Local Feature, Variance, Cross-correlation and Wavelet, Ph. D. Dissertation of University of Ulsan, 2009.
  3. H.S. Han, "Feature Vector Decision Method of Various Fault Signals for Neural-Network-Based Fault Diagnosis System," Journal of the Korean Society for Noise and Vibration Engineering, Vol. 20, No. 11, pp. 1009-1017, 2010. https://doi.org/10.5050/KSNVE.2010.20.11.1009
  4. I.A. Basheer and M. Hajmeer, "Artificial Neural Network: Fundamentals, Computing Design, and Application," Journal of Microbiological Methods, Vol. 43, pp. 3-31, 2000. https://doi.org/10.1016/S0167-7012(00)00201-3
  5. D.C. Baillie and J. Mathew, “A Comparison of Autoregressive Modeling Techniques for Fault Diagnosis of Rolling Element Bearings,” Mechanical Systems and Signal Processing, Vol. 10, No. 1, pp. 1-17, 1995. https://doi.org/10.1006/mssp.1996.0001
  6. S. Thanagasundram and F.S. Schlindwein, "Autoregressive Based Diagnositics Scheme for Detection of Bearing Faults," Proceedings of ISMA2006 Noise and Vibration Engineering Conference, pp. 3531-3546, 2006.
  7. H. Ocak and K.A. Loparo, "Estimation of the Running Speed and Bearing Defect Frequencies of an Induction Motor from Vibration Data," Mechanical Systems and Signal Processing, Vol. 18, pp. 515-533, 2004. https://doi.org/10.1016/S0888-3270(03)00052-9
  8. Q. Sun, Y. Tang, W.Y. Lu, and Y. Ji, "Feature Extraction with Discrete Wavelet Transform for Drill Wear Monitoring," Journal of Vibration and Control, Vol. 11, No. 11, pp. 1375-1390, 2005. https://doi.org/10.1177/1077546305058262
  9. M. Ge, G.C. Zhang, and Y, Yu., "Feature Extraction From Energy Distribution of Stamping Processes Using Wavelet Transform," Journal of Vibration and Control, Vol. 8. pp. 1323-1332, 2002.
  10. L. Dung, and M. Mizukawa, "A Pattern Recognition Neural Network Using Many Sets of Weights and Biases," Proceeding of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 285-290, 2007.
  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
  12. C.H. Lee, “Development of the Fault Diagnostic System on th Rotating Machinery Using Vibration Signal,” Journal of the Korean Society of Precision Engineering, Vol. 21, No. 12, pp. 75-76, 2004.
  13. S.S. Lee, Fault Diagnosis System of The Rotating Machines in Power Plant Using LPC, Master's Thesis of University of Ulsan, 2004.
  14. Y. Bengio, A. Courville, and P. Vincent, "Repersentation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue Learning Deep Architectures, pp. 1798-1828, 2013.
  15. G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, and N. Jaitly, et al.., "Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups," IEEE Signal Processing Magazine, Vol. 29, Issue 6, pp. 82-97, 2012. https://doi.org/10.1109/MSP.2012.2205597