Blocking detection of underground drainage pipe based on CEEMD and GG clustering

Regarding the difficulties in detecting the partial blockage in underground drainage pipeline and the degree of blocking , a novel method based on CEEMD and GG clustering is proposed in this paper. Firstly, the acoustical pressure signals collected from the pipeline were calculated to obtain the sound pressure level data, which were then decomposed by the complete ensemble empirical mode decomposition (CEEMD) ,the first 4 IMF components which selected by the Pearson's correlation coefficient and their energy proportion were extracted and will be used as the clustering features. Finally, the principle component analysis (PCA) was adopted to proceed the dimensionality reduction onto the feature vectors, and the GG (Gath-Geva) algorithm was applied to cluster the feature vectors into classes and to further identify the blocking conditions. The experiment results have suggested that the proposed method is capable of identifying partial blockage conditions of drainage pipeline in different degrees, and presents a certain value for the engineering applications.