Intelligent Identification Based on Wavelet Packet Decomposition and Adaptive Particle Swarm Optimization SVM

Abstract:

In this paper, a new Intelligent Identification method based on wavelet packet decomposition and APSO-SVM was put forward. As is known the characteristic of pressure drop is nonlinear and non-stationary. The wavelet packet transform can decompose signals to different frequency bands according to any time frequency resolution ratio, the features are extracted from the differential pressure fluctuation signals of the air-water two-phase flow in the horizontal pipe and the wavelet packet energy features of various flow regimes are obtained. The adaptive particle swarm ptimization support vector machine was trained using these eigenvectors as flow regime samples, and the flow regime intelligent identification was realized. The test results show the wavelet packet energy features can excellently reflect the difference between four typical flow regimes, and successful training the support vector machine can quickly and accurately identify four typical flow regimes. So a new way to identify flow regime by soft sensing is proposed.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 4043-4046
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.4043
• Online since: August 2013
• Authors: Qiang Wang, Li Jing Ren
• Keywords: Adaptive Particle Swarm Optimization (APSO), Flow Regime Identification, Support Vector Machine (SVM), Wavelet Packet Decomposition

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