Title :
Status Recognition for Electrical Parameters of ESPCP Based on Biomimetic Pattern Recognition
Author :
Shi Hai-tao ; Yu Yun-hua ; Kong Qian-qian
Author_Institution :
Coll. of Inf. & Control Eng., China Univ. of Pet. (East China), Dongying, China
Abstract :
Various fault types and difficult diagnosis restricted the improvement of economic benefit and system efficiency of electrical submersible progressing cavity pump (ESPCP) production system. A novel method for status recognition of electrical parameters in fault diagnosis of ESPCP based on biomimetic pattern recognition (BPR) is presented. Application results show the proposed BPR classifier produces significant accuracy for classification of ESPCP electrical parameters. Compared with the results based on support vector machine (SVM), the proposed method is more efficiency.
Keywords :
biomimetics; fault diagnosis; pattern recognition; pumps; ESPCP electrical parameters; biomimetic pattern recognition; electrical submersible progressing cavity pump production system; fault diagnosis; support vector machine; Biomimetics; Business process re-engineering; Electrostatic precipitators; Frequency; Neurons; Pattern recognition; Petroleum; Shape; Support vector machine classification; Support vector machines;
Conference_Titel :
Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5872-1
Electronic_ISBN :
978-1-4244-5874-5
DOI :
10.1109/IWISA.2010.5473236