基于仿生模式识别的ESPCP电参数状态识别

Hai-tao Shi, Yunhua Yu, Qian-qian Kong
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引用次数: 1

摘要

故障类型多,诊断困难,制约了电潜式螺杆泵生产系统经济效益和系统效率的提高。提出了一种基于仿生模式识别(BPR)的ESPCP故障诊断中电气参数状态识别的新方法。应用结果表明,所提出的BPR分类器对ESPCP电参数的分类具有较好的准确性。与基于支持向量机(SVM)的结果相比,该方法具有更高的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Status Recognition for Electrical Parameters of ESPCP Based on Biomimetic Pattern Recognition
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.
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