Outliers Detection of Dam Displacement Monitoring Data Based on Wavelet Transform

Abstract:

Dam safety monitoring data can be viewed as a digital signal sequence which consists of different frequency components. Identifying outliers to ensure the reliability of observational data becomes a foundation work of dam monitoring data analysis. Outliers of time signal series can be detected by wavelet transform. Lipschitz index can be used to measure the local singularity of a function, and the original abnormal signal can be found in the position of wavelet transform modulus maxima. Take horizontal displacement values observed by Lijiaxia concrete dam as example, an assumed error are added to the time series signal deliberately. A 4-level decomposition of the observation data was done by using wavelet db1, the results show that the modulus maxima occur at the given time. Therefore, outliers can be detected and located accurately by wavelet transform, which is important to analyze the safety monitoring data of dam.

• Periodical: Applied Mechanics and Materials (Volumes 71-78)
• Pages: 4590-4595
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.71-78.4590
• Online since: July 2011
• Authors: Jian Liu, Ji Jian Lian
• Keywords: Dam Monitoring, Lipschitz Index, Modulus Maxima, Outliers Detection, Wavelet Transform (WT)

References

[1] Daubechies, I., Orthonormal bases of compactly supported wavelets [J], Communication on pure and application mathematics, 1988, 41(7): 909-996.

DOI: 10.1002/cpa.3160410705

[2] Mallat, S. A theory for multi-resolution signal decomposition: the wavelet representation [J], IEEE Tran. on PAMI, 1989, 11(7): 674-693.

[3] S.G. Mallat, W. L. Hwang. Singularity detection and processing with wavelets [J], IEEE Trans. Inform. Theory, 1992, 38: 617 – 643.

DOI: 10.1109/18.119727

[4] S.J. Schiff, J. Heller, S.L. Weinstein, and J. Milton. Wavelet transforms and surrogate data for electroencephalographic spike and seizure detection [J], Opt. Eng., 1994. 33(7): 2162 -2169.

DOI: 10.1117/12.172248

[5] Wickerhouser, M.V., Adapted wavelet analysis from theory to software [M], New York, SIAM, 1994: 1-473.

[6] Donoho, D.L., Nonlinear wavelet methods for recovery of signals, densities and spectra from indirect and noisy data[J], Proceedings of symposia in applied mathematics and stanford report, 1993, 437.

DOI: 10.1090/psapm/047/1268002

[7] Zhang Qinghua, Benvenist. A, Wavelet networks [J], IEEE Trans. On neural networks, 1992, 3(6): 889-898.

[8] Yankui Sun. Wavelet analysis and its application [M]. Beijing, China machine press, (2005).

[9] Yuhua Peng. Wavelet transform and its application [M]. Beijing, Science press, (1999).

[10] Civil Engineering School of Tianjin University. Research on Safety Monitoring Models and Indexes for Lijiaxia Arch Dam, Tianjin, 2003. 11: 47-55.

[11] I. Daubechies. Ten Lectures on Wavelets [M]. SIAM, (1992).

[12] Defeng Zhang. Wavelet transform and its application based on matlab[M]. Beijing, National defence and industry press, (2008).