Title: Research on pipeline blocking state recognition algorithm based on mixed domain feature and KPCA-ELM

Authors: Jingzong Yang; Zao Feng; Xiaodong Wang; Guoyong Huang

Addresses: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 650500, Kunming, China; Engineering Research Center for Mineral Pipeline, Transportation of Yunnan Province, 650500, Kunming, China; School of Information, Baoshan University, Baoshan, 678000, Yunnan, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 650500, Kunming, China; Engineering Research Center for Mineral Pipeline, Transportation of Yunnan Province, 650500, Kunming, China; School of Information, Baoshan University, Baoshan, 678000, Yunnan, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 650500, Kunming, China; Engineering Research Center for Mineral Pipeline, Transportation of Yunnan Province, 650500, Kunming, China; School of Information, Baoshan University, Baoshan, 678000, Yunnan, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 650500, Kunming, China; Engineering Research Center for Mineral Pipeline, Transportation of Yunnan Province, 650500, Kunming, China; School of Information, Baoshan University, Baoshan, 678000, Yunnan, China

Abstract: Aiming at the problem of recognition on pipeline blockage, a method based on mixed domain feature and KPCA-ELM is proposed. Firstly, the original acoustic impulse response signals are analysed by statistical analysis and local mean decomposition (LMD), in order to construct the mixed domain features, which are made up of time, frequency and time-frequency domain features. Then the kernel principal component analysis (KPCA) is adopted to reduce the high-dimensional features of mixed domain and extract the main features which reflect the operation state of main components. Finally, the main features are input to extreme learning machine (ELM) for state recognition. After the feature extraction by KPCA, the redundancy of input features is eliminated. The simulation results show that KPCA is more sensitive to the nonlinear characteristics of the pipeline blockage signal when compared with PCA. Meanwhile, ELM is superior to BP in terms of classification accuracy and time consuming.

Keywords: pipeline; kernel principal component analysis; KPCA; extreme learning machine; ELM; state recognition.

DOI: 10.1504/IJCSM.2018.10016502

International Journal of Computing Science and Mathematics, 2018 Vol.9 No.5, pp.442 - 454

Received: 11 May 2017
Accepted: 17 Jun 2017

Published online: 28 Sep 2018 *

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