关键词: 自适应噪声完备集合经验模态分解, Lempel-Ziv 复杂度, 海鸥优化算法, 极限学习机, 管道信号

Abstract: Feature extraction is a troublesome problem in the pipe signal degrading the classification accuracy. To address this problem, a pipe signal diagnosis method that combines the signal processing method with the intelligence algorithm is proposed. Firstly, CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is used to decompose the signal to obtain several IMFs (Intrinsic Mode Functions) and the correlation coefficient method is used to select the useful mode function components and recombine them. Then the Lempel-Ziv complexity and Margin of the reconstructed signal are calculated as feature vector. Finally, the feature vector are inputted into the ELM ( Extreme Learning Machine ) optimized by SOA ( Seagull Optimization Algorithm) for classification. And validation is performed with laboratory data. Experimental results show that comparing with conventional ELM and GA-ELM(Extreme Learning Machine Optimized by Genetic Algorithm). SOA-ELM model can identify the pipe signals effectively, and has higher recognition rate and faster diagnosis speed. 

Key words: complete ensemble empirical mode decomposition with adaptive noise ( CEEMDAN)')">

complete ensemble empirical mode decomposition with adaptive noise ( CEEMDAN), lempel-ziv complexity, seagull optimization algorithm, extreme learning machine, pipeline signal