| 引用本文: | 于德亮,李妍美,丁宝,赵鹏舒,孙浩.潜油柱塞泵MMAGA-RBF故障诊断方法[J].哈尔滨工业大学学报,2017,49(9):159.DOI:10.11918/j.issn.0367-6234.201610123 |
| YU Deliang,LI Yanmei,DING Bao,ZHAO Pengshu,SUN Hao.The MMAGA-RBF fault diagnosis method for submersible plunger pump[J].Journal of Harbin Institute of Technology,2017,49(9):159.DOI:10.11918/j.issn.0367-6234.201610123 |
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| 潜油柱塞泵MMAGA-RBF故障诊断方法 |
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于德亮1,2,李妍美1,丁宝3,赵鹏舒1,孙浩1
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(1.哈尔滨理工大学 电气与电子工程学院,哈尔滨 150001;2.大庆油田采油工程研究院,黑龙江 大庆 163000; 3.哈尔滨工业大学 电气工程及自动化学院,哈尔滨 150001)
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| 摘要: |
| 针对潜油柱塞泵无法使用传统地面示功图方法进行故障诊断的问题，提出了一种适用于该抽油机的故障诊断方法.该方法首先利用多变异位自适应遗传算法(MMAGA)对RBF神经网络进行参数寻优，然后从潜油直线电机的运行参数和油井井口的仪表参数等综合数据中，提取出反映油泵运行状态的特征参数，并将其作为故障诊断模型的输入向量，从而实现潜油柱塞泵故障工况的诊断.结果表明: MMAGA-RBF故障诊断方法能够在较少的训练样本下达到较高的综合诊断准确率, 在训练样本集达到1 000组以上时, 其综合误判率可低于4%, 相对于普通遗传算法优化的模型, 其泛化能力更强. MMAGA-RBF故障诊断方法符合潜油柱塞泵的工况特点, 能够达到其综合诊断准确率的要求.
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| 关键词: 潜油柱塞泵 特征参数 故障诊断 RBF MMAGA |
| DOI：10.11918/j.issn.0367-6234.201610123 |
| 分类号:TP277 |
| 文献标识码:A |
| 基金项目:黑龙江省自然科学基金(E201305) |
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| The MMAGA-RBF fault diagnosis method for submersible plunger pump |
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YU Deliang1,2,LI Yanmei1,DING Bao3,ZHAO Pengshu1,SUN Hao1
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(1.College of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150001, China; 2.Research Institute of Oil Production Engineering Daqing Oilfield Company, Daqing 163000, Heilongjiang, China; 3.Institute of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)
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| Abstract: |
| As the traditional ground indicator diagram method is not suitable for fault diagnosis of submersible plunger pump, this paper proposes a new fault diagnosis method. Multiple Mutation Adaptive Genetic Algorithm(MMAGA)is used to optimize the weight and threshold values of Radial Basis Function neural network. Some characteristic parameters are then extracted from the operation parameters of the submersible linear motor and oil wellhead, which can reflect the working state of pump. These characteristic parameters are taken as input vectors of the new fault diagnosis model so as to realize the fault diagnosis of submersible plunger pump. The experimental results show that: the comprehensive diagnosis accuracy of MMAGA-RBF method is high when using less training samples, and the comprehensive misjudgment rate is lower than 4% when the training sample is above 1000 groups. The generalization ability is stronger compared to that of the traditional model. The MMAGA-RBF fault diagnosis method is suitable for the working condition characteristics and can meet the requirements of the comprehensive diagnosis accuracy of submersible plunger pump.
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| Key words: submersible plunger pump characteristic parameters fault diagnosis RBF MMAGA |
