Outliers Detection of Dam Displacement Monitoring Data Based on Wavelet Transform
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.
Dongye Sun, Wen-Pei Sung and Ran Chen
J. Liu and J. J. Lian, "Outliers Detection of Dam Displacement Monitoring Data Based on Wavelet Transform", Applied Mechanics and Materials, Vols. 71-78, pp. 4590-4595, 2011