Hybrid Method of Dynamograms Wavelet Analysis for Oil-production Equipment State Identification

被引：5
|
作者：
Korovin, I. S. [1 ]
Khisamutdinov, M., V [1 ]
机构：
[1] Southern Fed Univ SRI MCS SFEDU, Sci Res Inst Multiprocessor Comp Syst, Neural Syst Lab, Rostov Na Donu, Russia
来源：
MANUFACTURING AND APPLIED RESEARCH | 2014年 / 909卷
关键词：
Neural networks; Dynamogram; Sucker-rod oil pump; Malfunction detection; Wavelet analysis; Evolutionary algorithms;
D O I：
10.4028/www.scientific.net/AMR.909.252
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the paper we considered a hybrid method of dynamograms' wavelet analysis, applied for oil-production equipment work mode identification. Neural network architecture for sucker-rod deep-well pump units malfunction's detection is proposed. The architecture of intellectual data recognition system applied for pump installation control is presented.
引用
收藏
页码：252 / 259
页数：8
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