Novel Leakage Detection Method by Improved Adaptive Filtering and Pattern Recognition based on Acoustic Waves

Abstract

Pipeline leakages have plagued pipeline transportation for long time. Therefore, accurately extracting the features of leak signal in the presence of noise, and prompt identification of leak states and leak sizes is essential when leakage occurs. A novel leakage detection method based on the improved adaptive filter, whose parameters were optimized by the particle swarm optimization (PSO), was formulated and applied. The PSO-adaptive filter proved to be an effective signal processing method in contrast with variational mode decomposition (VMD). Its efficiency stems from the fact that the adaptive filter employs the noise collected from the detection environment. Therefore, the filter can adjust its parameters according to the changing situation. What is more, the application of PSO is conducive to automatically set suitable parameters for adaptive filter. After signal denoising, principal component analysis (PCA) was used for feature dimension reduction and selecting optimal features. The features after PCA proved to be more helpful in pattern recognition than the features without PCA. Furthermore, the relationship between the recognition results of leakage sizes and the measurement distance of the sensor was studied. Experimental results show that the method used in this paper can identify the leakage states with the accuracy of 100%. The identification result of leakage size reaches an accuracy of 86.75% under the influence of the measurement distance.