Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
권호 표지
DOI QR코드

DOI QR Code

Condition Monitoring of an LCD Glass Transfer Robot Based on Wavelet Packet Transform and Artificial Neural Network for Abnormal Sound

LCD 라인의 음향 특성신호에 웨이브렛 변환과 인경신경망회로를 적용한 공정로봇의 건정성 감시 연구

  • Received : 2012.03.26
  • Accepted : 2012.05.12
  • Published : 2012.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

Abnormal operating sounds radiated from a moving transfer robot in LCD (liquid crystal display) product lines have been used for the fault detection line of a robot instead of other source signals such as vibrations, acoustic emissions, and electrical signals. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone, despite a relatively low sensitivity. The wavelet packet transform for feature extraction and the artificial neural network for fault classification are employed. It can be observed that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.

LCD 생산라인의 공정 로봇에서 방사되는 비정상 작동 소음은 로봇의 결함 탐지에 사용된다. 이 신호의 장점은 상대적으로 낮은 민감도에 비해 단지 마이크로폰을 이용하여 다수의 결함을 확인할 수 있는 것이다. 결함요소 추출을 위한 웨이브렛 변환(WPT)과 불량의 분류를 위한 인공신경망 회로(ANN)이 본 논문에서 사용되었다. 결과적으로, 비정상 작동 소음이 기계요소의 결함 진단에 효율적으로 사용될 수 있다.

Keywords

References

  1. Rafiee, J. and Tse, P.W., 2009, "Use of Autocorrelation of Wavelet Coefficients for Fault Diagnosis," Mechanical Systems and Signal Processing, Vol. 23, No. 5, pp. 1554-1572. https://doi.org/10.1016/j.ymssp.2009.02.008
  2. Kim, M. S. and Lee, S. K., 2009, "Detection of Leak Acoustic Signal in Buried Gas Pipe Based on the Time-Frequency Analysis," Journal of Loss Prevention in the Process Industries, Vol. 22, No. 6, pp.990-994. https://doi.org/10.1016/j.jlp.2008.08.009
  3. Mallat, S.G., 1989, "A Theory of Multi-Resolution Signal Decomposition: the Wavelet Representation," IEEE Transactions on Pattern and Machine Intelligence, Vol. 11, No. 7, pp. 674-693. https://doi.org/10.1109/34.192463
  4. Wickerhauser, M. V. and Coifman, P. R., 1992, "Entropy Based Methods for Best Basis Selection," IEEE Transactions on Information Theory, Vol. 38, No. 2, pp. 713-718. https://doi.org/10.1109/18.119732
  5. Daubechies, I., 1990, "The Wavelet Transformation, Time-Frequency Localization and Signal Analysis," IEEE Transaction on Information Theory, Vol 36, pp. 961-1005. https://doi.org/10.1109/18.57199
  6. Shannon, C.E. and Weaver, W., 1949, Mathematical Theory of Communication, University of Illinois Press, Urbana.
  7. Bishop, C. M., 1995, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK.
  8. Matrn, H., 1996, Neural Network Design. PWS Publishing Company, Boston, USA.
  9. Lee, H.-H., Kim, S.-J. and Lee, S.-K., 2009, "Design of New Sound Metric and Its Application for Quantification of an Axle Gear Whine Sound by Utilizing Artificial Neural Network," Journal of Mechanical Science and Technology, Vol. 23, No. 4, pp. 1182-1193. https://doi.org/10.1007/s12206-009-0106-0