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Development of Integrated Outlier Analysis System for Construction Monitoring Data

건설 계측 데이터에 대한 통합 이상치 분석 시스템 개발

  • Jeon, Jesung (Department of Construction and Safety Engineering, Induk University)
  • Received : 2020.03.09
  • Accepted : 2020.04.21
  • Published : 2020.05.01

Abstract

Outliers detection and elimination included in field monitoring datum are essential for effective foundation of unusual movement, long and short range forecast of stability and future behavior to various structures. Integrated outlier analysis system for assessing long term time series data was developed in this study. Outlier analysis could be conducted in two step of primary analysis targeted at single dataset and second multi datasets analysis using synthesis value. Integrated outlier analysis system presents basic information for evaluating stability and predicting movement of structure combined with real-time safety management platform. Field application results showed increased correlation between synthesis value including similar sort of sensor showing constant trend and each single dataset. Various monitoring data in case of showing different trend can be used to analyse outlier through correlation-weighted value.

구조물의 이상징후 판단 및 장단기 안정성, 장래 거동 등의 판단에 다양한 계측결과가 효율적으로 이용되기 위해서는 계측 데이터 내에 포함한 각종 이상치의 판정 및 제거가 필요하다. 본 연구에서는 장기 시계열 데이터에 대한 이상치 평가를 수행하기 위한 통합 이상치 분석 시스템을 개발하였다. 이상치 평가는 시계열 분석법에 의한 단일 데이터셋 대상의 1차 이상치 분석과 합성신호 기반의 다중 데이터셋에 대한 2차 이상치 분석으로 구분하여 단계별로 수행되었다. 통합 이상치 분석 시스템은 구조물에 대한 종합 안전관리 플랫폼과 실시간 연동되어 구조물의 각종 안전성 평가 및 거동 예측 등을 위한 기초자료를 제공할 수 있도록 개발되었다. 현장 적용을 통해 일정 경향을 보이는 동종의 다수 센서들의 합성신호와 개별 데이터셋 간의 상관성이 크게 증가함을 확인할 수 있었으며, 상관성에 대한 가중치 적용을 통해 차별 거동을 보이는 다양한 센서 계측치들도 모두 통합 이상치 분석에 활용될 수 있음을 확인 할 수 있었다.

Keywords

References

  1. Jeon, J. S., Koo, J. K. and Park, C. M. (2015), Outlier detection in time series monitoring datasets using rule based and correlation analysis method, Journal of the Korean Geo-Environmental Society, Vol. 16, No. 5, pp. 43-53 (In Korean). https://doi.org/10.14481/jkges.2015.16.5.43
  2. Jeon, J. S. (2016), Development of Outlier Evaluation Technique and Operation System for Monitoring Data, KSCE 2016 Convention, Jeju, Korea, pp. 355-356 (In Korean).
  3. Jeon, J. S. (2018), Compound outlier assessment and verification for multiple field monitoring data, Journal of the Korean Geo-Environmental Society, Vol. 19, No. 1, pp. 5-14 (In Korean).
  4. Kailath, T. (1975), Square-root algorithms for least-squares estimation, IEEE Trans. Automatic Control, Vol. 20, No. 4, pp. 487-497. https://doi.org/10.1109/TAC.1975.1100994
  5. Kang, P. S., Kim, D. I., Lee, S. K., Doh, S. Y. and Cho, S. J. (2012), Estimating the reliability of virtual metrology predictions in semiconductor manufacturing : a novelty detection-based approach, Journal of Korean Institute of Industrial Engineers, Vol. 38, No. 1, pp. 46-56 (In Korean). https://doi.org/10.7232/JKIIE.2012.38.1.046
  6. Ki, Y. K., Ahn, G. H., Kim, E. J., Jeong, J. H., Bae, K. S. and Lee, C. K. (2010), Error filtering algorithm for accurate travel speed measurement using UTIS, Journal of The Korea Institute of Intelligent Transport Systems, Vol. 9, No. 6, pp. 33-42 (In Korean).
  7. Lim, H. S., Oh, C., Park, J. H. and Lee, G. W. (2009), Correction of erroneous individual vehicle speed data using locally weighted regression, Journal of Korean Society of Transportation, Vol. 27, No. 2, pp. 47-56 (In Korean).
  8. Mourad, M. and Bertrand-Krajewski, J.-L. (2002), A method for automatic validation of long time series of data in urban hydrology, Water Science and Technology, Vol. 45, No. 4-5, pp. 263-270. https://doi.org/10.2166/wst.2002.0601
  9. Ni, k., Ramanathan, N., Chehade, M., Balzano, L., Nair, S., Zahedi, S., Pottie, G., Hansen, M. and Srivastava, M. (2009), Sensor network data fault types, ACM Transactions on Sensor Networks, Vol. 5, No. 3, Article 25. pp. 1-29.
  10. Ramanathan, N., Balzano, L., Burt, M., Estrin, D., Kohler, E., Harmon, T., Harvey, C., Jay, J., Rothenberg, S. and Srivastava, M. (2006), Rapid Deployment with Confidence: Calibration and Fault Detection in Environmental Sensor Networks. Tech. Rep. 62, CENS. pp. 1-14.
  11. Sharma, A. B., Golubchik, L. and Govindan, R. (2010), Sensor faults: detection methods and prevalence in real-world datasets, ACM Transactions on Sensor Networks, Vol. 6, No. 3, Article 23. pp. 1-39.
  12. Williams, G. J., Baxter, R. A., He, H. X., Hawkins, S. and Gu, L. (2002), A Comparative Study of RNN for Outlier Detection in Data Mining, IEEE International Conference on Data-mining (ICDM'02), Maebashi City, Japan, CSIRO Technical Report CMIS-02/102. pp. 1-709.