Aiming at the detection problem of blockage within the urban water supply pipeline in the early stage

a method combined component signal feature extraction with support vector machine（ SVM） for blockage recognition based on local mean decomposition（ LMD） is proposed. The first step of this method is to decompose the acoustic response signal by LMD

so that a number of PF components can be obtained. Then the correlation analysis method is used to select the effective PF components. The characteristics of energy entropy

approximate entropy and average sound pressure of the effective PF components are extracted respectively

so the classification feature sets can be constructed. Finally

the cross validation（ CV） is used to optimize the parameters of the SVM classifier to identify the blockage fault signal. The results from the experiments have shown that the method can identify the blockage in the water supply pipeline effectively based on the combination usage of LMD component features and cross validation SVM. In addition

the method is compared with the method based on LMD feature fusion and BP neural network and the results suggest that the proposed method has a better performance on the partial blockage recognition.