A Novel Soft Sensing Based on Wavelet Packet Decomposition

DOI

10.2991/icmia-17.2017.20How to use a DOI?

Keywords

Flow regime identification; Wavelet packet decomposition; support vector machine; Soft sensing.

Abstract

In this paper, a new soft sensing method based on wavelet packet decomposition and SVM was put forward. As is known the characteristic of pressure drop is nonlinear and non-stationary. Based on the characteristics that the wavelet packet transform can decompose signals to different frequency bands according to any time frequency resolution ratio, the concept and the algorithm of the wavelet packet energy features are proposed. At the same time, 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 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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Qiang Wang
PY  - 2017/06
DA  - 2017/06
TI  - A Novel Soft Sensing Based on Wavelet Packet Decomposition
BT  - Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SP  - 113
EP  - 116
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.20
DO  - 10.2991/icmia-17.2017.20
ID  - Wang2017/06
ER  -