改进连续隐马尔可夫模型的有杆抽油故障诊断

王东宇; 刘宏昭; 任慧

doi:10.13433/j.cnki.1003-8728.20220155

改进连续隐马尔可夫模型的有杆抽油故障诊断

doi: 10.13433/j.cnki.1003-8728.20220155

王东宇^{1, 2,},
刘宏昭^1, ,,
任慧³

1.
西安理工大学机械与精密仪器工程学院，西安　710048
2.
中国石油集团测井有限公司测井技术研究院，西安　710077
3.
西安财经大学信息学院，西安　710100

基金项目: 国家自然科学基金项目（51275404）与陕西省“13115”重大科技专项（2009ZDKGG-33）

详细信息

作者简介:
王东宇（1987−），博士研究生，研究方向为有杆抽油系统动力学分析及故障诊断，dongyu-wang@qq.com

通讯作者:
刘宏昭，教授，博士生导师，liu-hongzhao@163.com

中图分类号: TE933.3; TH165.3
计量
- 文章访问数: 137
- HTML全文浏览量: 39
- PDF下载量: 49
- 被引次数: 0
出版历程
- 收稿日期: 2021-07-02
- 刊出日期: 2023-12-25

Fault Diagnosis of Sucker Rod Pumping System Using Improved Continuous Hidden Markov Model

WANG Dongyu^{1, 2
,},
LIU Hongzhao^{1
, ,},
REN Hui³

1.
School of Mechanical and Precision Instrument Engineering, Xi′an University of Technology, Xi′an 710048, China
2.
Logging Technology Research Institute, China Petroleum Logging Co., Ltd., Xi′an 710077, China
3.
School of Information, Xi'an University of Finance and Economics, Xi′an 710100, China

摘要

摘要: 利用人工智能诊断有杆抽油系统故障时，描述工况的示功图成为机器学习的对象，提取示功图特征和建立诊断模型是主要步骤。已有的依据阀门工作位置的几何特征未能直接反映示功图的面积，因此提出一组改进的训练特征，并采用连续隐马尔可夫模型(CHMM)建立诊断模型。为了使参数的初始化更可靠，使用与混合高斯模型相关联的K-means聚类算法。将本文提出的诊断方法用于真实油井的示功图集进行测试，结果表明，本文方法不仅有效，而且改进的训练特征和建模方法都提高了诊断的准确率。
- 示功图 /
- 特征提取 /
- 连续隐马尔可夫模型 /
- K-means聚类
Abstract: When using artificial intelligence to diagnose the fault of sucker rod pumping system, dynamometer cards describing the working condition become the object of machine learning, and the main steps are to extract the feature of dynamometer card and build the diagnosis model. The existing geometric features based on valve working position cannot directly reflect the area of dynamometer card, so a group of improved training features are proposed, and the continuous hidden Markov model (CHMM) is used to establish the diagnosis model. In order to make the initialization of parameters more reliable, the K-means clustering algorithm associated with the Gaussian mixture model is used. The diagnosis method proposed in this paper is used to test the dynamometer card set of real oil wells, the results show that this method is effective, and the improved training features and modeling methods can enhance the accuracy of fault diagnosis
- dynamometer card /
- feature extraction /
- continuous hidden Markov model /
- K-means clustering

HTML全文

图 1 理论示功图

Figure 1. Theoretical dynamometer diagram

下载: 全尺寸图片幻灯片

图 2 基于重心分区的示功图

Figure 2. Dynamometer diagram based on center of gravity segmentation

下载: 全尺寸图片幻灯片

图 3 KB-CHMM训练流程图

Figure 3. Flow chart of KB-CHMM training

下载: 全尺寸图片幻灯片

图 4 有杆抽油故障诊断框架图

Figure 4. Fault diagnosis framework of sucker rod pumping system

下载: 全尺寸图片幻灯片

表 1 训练数据集

Table 1. Training data set

编号	工况类型	示功图数量
1	正常	20
2	气体影响	32
3	供液不足	28
4	固定阀漏	16
5	游动阀漏	24
6	油管漏	22
7	油杆断	32
总数		174

下载: 导出CSV

表 2 训练示功图几何特征的平均值

Table 2. Average value of geometric features for training dynamometers

工况	几何特征
工况	S_ABCD	S_L-up	S_R-up	S_R-down	S_L-down	D_BC	D_DA
正常	68.47	8.38	6.75	11.14	5.51	8.27	7.83
气体影响	60.36	7.08	8.58	23.45	4.35	8.29	5.14
供液不足	51.70	5.54	5.88	33.70	8.81	8.72	3.02
固定阀漏	62.41	7.48	8.57	9.52	3.85	9.11	8.08
游动阀漏	63.28	9.75	5.85	5.11	5.32	8.84	9.50
油管漏	54.16	8.97	20.07	7.85	9.52	10.3	10.1
油杆断	28.82	29.5	29.86	3.67	3.59	9.50	9.65

下载: 导出CSV

表 3 实测示功图诊断结果

Table 3. Diagnostic results of measured dynamometers

诊断工况实际工况	正常	气体影响	供液不足	固定阀漏	游动阀漏	油管漏	油杆断
正常	5	0	0	0	0	0	0
气体影响	1	6	0	0	0	0	0
供液不足	0	0	6	0	0	0	0
固定阀漏	0	0	0	6	0	0	0
游动阀漏	0	0	0	0	6	0	0
油管漏	0	0	0	0	0	6	0
油杆断	0	0	0	0	0	0	6

下载: 导出CSV

表 4 KB-CHMM诊断方法与其他诊断方法的对比

Table 4. Comparison of KB-CHMM diagnostic method with other diagnostic methods

方法	训练的几何特征
方法	曲线矩	文献[13]	本文提出
HMM	64.2%	71.4%	75.2%
CHMM	71.4%	82.1%	83.6%
CSA-CHMM	82.1%	94.1%	96.2%
KB-CHMM	83.9%	96.2%	97.6%

下载: 导出CSV

参考文献(18)

[1]	萧河. 2020年全球能源需求将出现70年最大降幅[J]. 中国石化, 2020(5): 86. XIAO H. Global energy demand will show the largest decline in 70 years in 2020[J]. Sinopec Monthly, 2020(5): 86. (in Chinese)
[2]	贾承造. 中国石油工业上游发展面临的挑战与未来科技攻关方向[J]. 石油学报, 2020, 41(12): 1445-1464. JIA C Z. Development challenges and future scientific and technological researches in China's petroleum industry upstream[J]. Acta Petrolei Sinica, 2020, 41(12): 1445-1464. (in Chinese)
[3]	李阳, 廉培庆, 薛兆杰, 等. 大数据及人工智能在油气田开发中的应用现状及展望[J]. 中国石油大学学报(自然科学版), 2020, 44(4): 1-11. LI Y, LIAN P Q, XUE Z J, et al. Application status and prospect of big data and artificial intelligence in oil and gas field development[J]. Journal of China University of Petroleum, 2020, 44(4): 1-11. (in Chinese)
[4]	张凯, 赵兴刚, 张黎明, 等. 智能油田开发中的大数据及智能优化理论和方法研究现状及展望[J]. 中国石油大学学报(自然科学版), 2020, 44(4): 28-38. ZHANG K, ZHAO X G, ZHANG L M, et al. Current status and prospect for the research and application of big data and intelligent optimization methods in oilfield development[J]. Journal of China University of Petroleum, 2020, 44(4): 28-38. (in Chinese)
[5]	杨剑锋, 杜金虎, 杨勇, 等. 油气行业数字化转型研究与实践[J]. 石油学报, 2021, 42(2): 248-258. YANG J F, DU J H, YANG Y, et al. Research and practice on digital transformation of the oil and gas industry[J]. Acta Petrolei Sinica, 2021, 42(2): 248-258. (in Chinese)
[6]	曾涛, 刘晗光, 高坚. 斯伦贝谢公司数字化转型的经验与启示[J]. 国际石油经济, 2021, 29(1): 94-99. ZENG T, LIU H G, GAO J. Experience and inspiration from Schlumberger's digital transition[J]. International Petroleum Economics, 2021, 29(1): 94-99. (in Chinese)
[7]	张朝林, 范玉刚. CEEMD与卷积神经网络特征提取的故障诊断方法研究[J]. 机械科学与技术, 2019, 38(2): 178-183. ZHANG Z L, FAN Y G. Fault diagnosis of a bearing using feature extraction method based on CEEMD algorithm and CNN[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(2): 178-183. (in Chinese)
[8]	HOANG D T, KANG H J. A survey on deep learning based bearing fault diagnosis[J]. Neurocomputing, 2019, 335: 327-335. doi: 10.1016/j.neucom.2018.06.078
[9]	LI K, HAN Y, WANG T. A novel prediction method for down-hole working conditions of the beam pumping unit based on 8-directions chain codes and online sequential extreme learning machine[J]. Journal of Petroleum Science and Engineering, 2018, 160: 285-301. doi: 10.1016/j.petrol.2017.10.052
[10]	LI K, GAO X W, ZHOU H B, et al. Fault diagnosis for down-hole conditions of sucker rod pumping systems based on the FBH-SC method[J]. Petroleum Science, 2015, 12(1): 135-147. doi: 10.1007/s12182-014-0006-5
[11]	高银中, 雷小强, 杨江天. 基于泵示功图的抽油机井井口产量计算[J]. 石油矿场机械, 2008, 37(5): 88-92. GAO Y Z, LEI X Q, YANG J T. Production evaluation of oil wells using dynamometer card[J]. Oil Field Equipment, 2008, 37(5): 88-92. (in Chinese)
[12]	LIANG H, LI X M. Accurate extraction of valve opening and closing points based on the physical meaning of surface dynamometer card[J]. Petroleum Exploration and Development, 2011, 38(1): 109-115. doi: 10.1016/S1876-3804(11)60018-9
[13]	ZHENG B Y, GAO X W. Sucker rod pumping diagnosis using valve working position and parameter optimal continuous hidden Markov model[J]. Journal of Process Control, 2017, 59: 1-12. doi: 10.1016/j.jprocont.2017.09.007
[14]	周斌, 王延江, 刘伟锋, 等. 基于Hessian正则化支持向量机的多视角协同识别抽油机井工况方法[J]. 石油学报, 2018, 39(12): 1429-1436. ZHOU B, WANG Y J, LIU W F, et al. A working condition recognition method of sucker-rod pumping wells based on Hessian-regularized SVM and multi-view co-training algorithm[J]. Acta Petrolei Sinica, 2018, 39(12): 1429-1436. (in Chinese)
[15]	XU P, XU S J, YIN H W. Application of self-organizing competitive neural network in fault diagnosis of suck rod pumping system[J]. Journal of Petroleum Science and Engineering, 2007, 58(1-2): 43-48. doi: 10.1016/j.petrol.2006.11.008
[16]	YUWONO M, QIN Y, ZHOU J, et al. Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model[J]. Engineering Applications of Artificial Intelligence, 2016, 47: 88-100. doi: 10.1016/j.engappai.2015.03.007
[17]	HUANG D R, KE L Y, CHU X Y, et al. Fault diagnosis for the motor drive system of urban transit based on improved Hidden Markov Model[J]. Microelectronics Reliability, 2018, 82: 179-189. doi: 10.1016/j.microrel.2018.01.017
[18]	肖文斌. 基于耦合隐马尔可夫模型的滚动轴承故障诊断与性能退化评估研究[D]. 上海: 上海交通大学, 2011: 30. XIAO W B. Study on coupled hidden Markov model based rolling element bearing fault diagnosis and performance degradation assessment[D]. Shanghai: Shanghai Jiao Tong University, 2011: 30. (in Chinese)