Sparse representation of acoustic emission signals and its application in pipeline leak location

The acoustic detection of pipeline leaks is strongly influenced by the associated interference and noise. In this paper, acoustic emission signals are analyzed using a sparse representation method and the main components associated with the leakage are extracted. Dictionary learning is performed using training samples composed of the leakage signals and noise signals. The measured signal is sparsely decomposed on the composite dictionary, allowing the main leakage components to be estimated. The effects of dictionary dimensionality, redundancy, sparsity constraints, and other parameters on the performance of the sparse representation algorithm are investigated. Finally, the leak location in the pipeline is determined through cross-correlation analysis of the reconstructed acoustic signals. Experimental results show that the proposed sparse representation method effectively improves the signal-to-noise ratio of the acoustic emission signals, and correspondingly improves the accuracy and reliability of pipeline leak location compared with traditional localization methods.