Research on feature extraction of indicator card data for sucker-rod pump working condition diagnosis

Authors:
Yunhua Yu;Haitao Shi;Lifei Mi
Affiliations:
College of Information & Control Engineering, China University of Petroleum, Qingdao, China;College of Information & Control Engineering, China University of Petroleum, Qingdao, China;College of Information & Control Engineering, China University of Petroleum, Qingdao, China
Venue:
Journal of Control Science and Engineering - Special issue on Advances in Methods for Networked and Cyber-Physical System
Year:
2013

Citing 2
Cited 0

Fundamentals of digital image processing

Fundamentals of digital image processing
An Experimental Comparison of Autoregressive and Fourier-Based Descriptors in 2D Shape Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Three feature extraction methods of sucker-rod pump indicator card data have been studied, simulated, and compared in this paper, which are based on Fourier Descriptors (FD), Geometric Moment Vector (GMV), and Gray Level Matrix Statistics (GLMX), respectively. Numerical experiments show that the Fourier Descriptors algorithm requires less running time and less memory space with possible loss of information due to nonoptimal numbers of Fourier Descriptors, the Geometric Moment Vector algorithm is more time-consuming and requires more memory space, while the Gray Level Matrix Statistics algorithm provides low-dimension feature vectors with more time consumption and more memory space. Furthermore, the characteristic of rotational invariance, both in the Fourier Descriptors algorithm and the Geometric Moment Vector algorithm, may result in improper pattern recognition of indicator card data when used for sucker-rod pump working condition diagnosis.