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/).
Cite this article
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 -