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Stress Detection of Railway Point Machine Using Sound Analysis

소리 정보를 이용한 철도 선로전환기의 스트레스 탐지

  • Received : 2016.06.29
  • Accepted : 2016.07.20
  • Published : 2016.09.30

Abstract

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure can significantly affect railway operations with potentially disastrous consequences, early stress detection of point machine is critical for monitoring and managing the condition of rail infrastructure. In this paper, we propose a stress detection method for point machine in railway condition monitoring systems using sound data. The system enables extracting sound feature vector subset from audio data with reduced feature dimensions using feature subset selection, and employs support vector machines (SVMs) for early detection of stress anomalies. Experimental results show that the system enables cost-effective detection of stress using a low-cost microphone, with accuracy exceeding 98%.

철도 선로전환기는 열차의 진로를 현재의 궤도에서 다른 궤도로 제어하는 장치이다. 선로전환기의 이상 상황은 탈선 등과 같은 심각한 문제를 발생할 수 있기 때문에, 선로전환기의 스트레스를 지속적으로 모니터링 하는 것은 매우 중요하다. 본 논문에서는 선로전환기가 작동할 때 발생하는 소리 정보를 이용하여 선로전환기의 스트레스를 탐지하는 시스템을 제안한다. 제안하는 시스템은 선로전환기의 동작 시 발생하는 소리 데이터로부터 자질 선택방법을 사용하여 스트레스 탐지에 유효한 감소된 차원의 자질 부분집합을 선택한 후, 기계학습의 대표적 모델인 SVM(Support Vector Machine)을 이용하여 선로전환기의 스트레스 상태 여부를 탐지한다. 테스트용 선로전환기를 실제 구동하며 수집한 소리 데이터를 이용하여, 본 논문에서 제안하는 시스템의 성능을 실험적으로 검증한 바 98%를 넘는 정확도를 확인하였다.

Keywords

References

  1. J. H. Lee and Y. K. Kim, "A study on switching power measurement of an electrical point machine using a sensor," Journal of the Korean Society for Railway, Vol.18, No.4, pp.335-343, 2015. https://doi.org/10.7782/JKSR.2015.18.4.335
  2. J. H. Lee, Y. K. Kim, and J. Y. Park, "A study on the field application of switching power measurement by using sensor in electrical point machine," The Transactions of the Korean Institute of Electrical Engineers, Vol.64, No.7, pp.1130-1136, 2015. https://doi.org/10.5370/KIEE.2015.64.7.1130
  3. C. S. Kim and G. H. Kang, "Fatigue analysis of reduction gears unit in rolling stock considering operating characteristics," Journal of the Korea Academia Industrial Cooperation Society, Vol.12, No.3, pp.1085-1090, 2011. https://doi.org/10.5762/KAIS.2011.12.3.1085
  4. W. Jin, Z. Shi, D. Siegel, P. Dersin, C. Douziech, M. Pugnaloni, and J. Lee, "Development and evaluation of health monitoring techniques for railway point machines," in Prognostics and Health Management 2015 IEEE Conference, pp.1-11, 2015.
  5. M. Vileiniskis, R. Remenyte-Prescott, and D. Rama, "A fault detection method for railway point systems," in Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, Vol.230, No.3, pp.852-865, 2016. https://doi.org/10.1177/0954409714567487
  6. O. Eker, F. Camci, and U. Kumar, "SVM based diagnostics on railway turnouts," International Journal of Performability Engineering, Vol.8, No.3, pp.289-398, 2012.
  7. T. Asada and C. Roberts, "Improving the dependability of DC point machines with a novel condition monitoring system," Proceedings of the Institution of Mechanical Engineers, Part F: Journal of rail and rapid transit, Vol.227, No.4, pp.322-332, 2013. https://doi.org/10.1177/0954409713481748
  8. T. Asada, C. Roberts, and T. Koseki, "An algorithm for improved performance of railway condition monitoring equipment: Alternating-current point machine case study," Transportation Research Part C: Emerging Technologies, Vol.30, pp.81-92, 2013. https://doi.org/10.1016/j.trc.2013.01.008
  9. J. Lee, H. Choi, D. Park, Y. Chung, H.-Y. Kim, and S. Yoon, "Fault detection and diagnosis of railway point machines by sound analysis," Sensors, Vol.16, No.4, pp.549, 2016. https://doi.org/10.3390/s16040549
  10. J. Lee, L. Jin, D. Park, Y. Chung, and H. Chang, "Acoustic features for pig wasting disease detection," International Journal of Information Processing and Management, Vol.6, No.1, pp.37-46, 2015.
  11. J. Lee, B. Noh, S. Jang, D. Park, Y. Chung, and H.-H. Chang, "Stress detection and classification of laying hens by sound analysis," Asian-Australasian Journal of Animal Sciences, Vol.28, No.4, pp.592-598, 2015. https://doi.org/10.5713/ajas.14.0654
  12. S. Theodoridis and K. Koutroumbas, "Pattern Recognition," 4th ed, Oxford: Academic Press, 2009.
  13. J. Han, M. Kamber, and J. Pei, "Data Mining: Concepts and Techniques," 3rd ed, San Francisco: Morgan Kaufmann Publishers, 2012.
  14. H. Kim, S. Lee, Y. Chung, D. Park, and H. Lee, "Multicore Processor based Parallel SVM for Video Surveillance System," Journal of the Korea Institute of Information Security and Cryptology, Vol.21, No.6, pp.161-169, 2011.