Smart oilfield equipment health management method based on big data

SONG Shiji; YANG Le; PAN Xuran; HAN Yizeng; HUANG Zhong

doi:10.12405/j.issn.2097-1486.2022.02.005

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Smart oilfield equipment health management method based on big data

更新时间：2024-11-01

- Smart oilfield equipment health management method based on big data
- Emerging Science and Technology Vol. 1, Issue 2, Pages: 178-192(2022)
- 作者机构：
  
  1.清华大学自动化系，北京　100084
  2.西安交通大学信息与通信工程学院，陕西　西安　710049
  3.重载快捷大功率电力机车全国重点实验室，中车株洲电力机车有限公司，湖南　株洲　412001
- 作者简介：
- 基金信息：
- DOI：10.12405/j.issn.2097-1486.2022.02.005
  CLC： TP311.13
- Published：2022-12，
  
  Received：10 August 2022，
  
  Revised：22 September 2022，
- 稿件说明：
移动端阅览
SONG Shiji, YANG Le, PAN Xuran, et al. Smart oilfield equipment health management method based on big data. [J]. Emerging Science and Technology, 2022,1(2):178-192.
DOI：

SONG Shiji, YANG Le, PAN Xuran, et al. Smart oilfield equipment health management method based on big data. [J]. Emerging Science and Technology, 2022,1(2):178-192. DOI： 10.12405/j.issn.2097-1486.2022.02.005.

摘要

油田资源勘探过程是一个复杂的大系统，包括油气生产全过程的数据采集、处理、分析、存储、应用等多个环节，生产过程数据复杂、异构，具有数据不完备和时空分布等特征。本文探讨冗余数据处理技术、缺失数据处理技术、分布式数据融合技术的特点，结合油气生产过程的典型案例给出的实际数据，给出复杂、异构且不完备数据处理方法在油田生产设备状态监测、诊断及预测中的应用论证评估。进而利用复杂异构数据的分布式数据库管理技术，建立生产设备状态监测、诊断及预测的数据资源管理框架、数据处理流程及相关数据技术规范。

Abstract

The process of oil field resource exploration is a complex large-scale system

including multiple links of data acquisition

processing

analysis

storage and application in the whole process of oil and gas production. The data in the production process is complex and heterogeneous

with the characteristics of incomplete data and temporal and spatial distribution. The paper discusses the characteristics of redundant data processing technology

missing data processing technology and distributed data fusion technology. Combined with the actual data given by typical cases of oil and gas production process

the paper gives the application demonstration and evaluation of complex

heterogeneous and incomplete data processing methods in condition monitoring

diagnosis and prediction of oilfield production equipment. Then

using the distributed database management technology of complex heterogeneous data

the data resource management framework

data processing flow and relevant data technical specifications for production equipment condition monitoring

diagnosis and prediction are established.

关键词

油田资源勘探数据融合分布式数据库管理

Keywords

oil field resource explorationdata fusiondistributed database management

references

SALVANESCHI P, CEDEI M, LAZZARI M. Applying AI to structural safety monitoring and evaluation[J]. IEEE expert, 1996, 11(4): 24-34. doi: 10.1109/64.511774http://doi.org/10.1109/64.511774.

WEBER D P. Fuzzy fault tree analysis[C]. Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence, Proceedings of the Third IEEE Conference on. IEEE, 1994: 1899-1904.

SUN Q, TANG Y. Singularity analysis using continuous wavelet transform for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2002, 16(6):1025-1041. doi: 10.1006/mssp.2002.1474http://doi.org/10.1006/mssp.2002.1474.

TENNEY R R. Incipient Failure Detection Using Wavelets[R].DTIC Document, 1992.

WANG W J, MCFADDEN P D. Application of wavelets to gearbox vibration signals for fault detection[J]. Journal of Sound and Vibration, 1996, 192(5):927-939. doi: 10.1006/jsvi.1996.0226http://doi.org/10.1006/jsvi.1996.0226.

WANG W J, MCFADDEN P D. Application of orthogonal wavelets to early gear damage detection[J]. Mechanical Systems And Signal Processing, 1995, 9(5):497-507. doi: 10.1006/mssp.1995.0038http://doi.org/10.1006/mssp.1995.0038.

LOPEZ J E, TENNEY R R, DECKERT J C. Fault detection and identification using real-time wavelet feature extraction[C]. Proceedings of IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis. IEEE, 1994: 217-220.

谭阳红, 叶佳卓. 模拟电路故障诊断的小波方法[J]. 电子与信息学报, 2006(09):1748-1751.

AL-RAHEEM K F, ROY A, RAMACHANDRAN K P, et al. Application of the Laplace-wavelet combined with ANN for rolling bearing fault diagnosis[J]. Journal of Vibration and Acoustics, 2008, 130:51007. doi: 10.1115/1.2948399http://doi.org/10.1115/1.2948399.

PARIKH U B, DAS B, PRAKASH MAHESHWARI R P. Combined wavelet-SVM technique for fault zone detection in a series compensated transmission line[J]. Power Delivery, IEEE Transactions on, 2008, 23(4):1789-1794. doi: 10.1109/TPWRD.2008.919395http://doi.org/10.1109/TPWRD.2008.919395.

PRADHAN A K, ROUTRAY A, PATI S, et al. Wavelet fuzzy combined approach for fault classification of a series-compensated transmission line[J]. Power Delivery, IEEE Transactions on, 2004, 19(4):1612-1618. doi: 10.1109/TPWRD.2003.822535http://doi.org/10.1109/TPWRD.2003.822535.

李智, 陈祥初. 包络分析及其在设备故障诊断中的应用[J]. 测试技术学报, 2002,16(002):92-95.

ROBERTS N H, VESELY W E. Fault tree handbook[M]. Government Printing Office, 1987.

BURDICK G R, FUSSELL J B, RASMUSON D M, et al. Phased mission analysis: a review of new developments and an application[J]. IEEE Transactions on Reliability, 1977, 26(1): 43-49. doi: 10.1109/TR.1977.5215072http://doi.org/10.1109/TR.1977.5215072.

WISE B M. A theoretical basis for the use of principal component models for monitoring multivariate processes[J]. Process control and quality, 1990. 1(1): 41-51.

KU W, STORER R H, GEORGAKIS C. Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics and intelligent laboratory systems, 1995. 30(1): 179-196. doi: 10.1016/0169-7439(95)00076-3http://doi.org/10.1016/0169-7439(95)00076-3.

MCNABB C A, QIN S J. Fault diagnosis in the feedback-invariant subspace of closed-loop systems[J]. Industrial & engineering chemistry research, 2005. 44(8): 2359-2368.

CHERRY G A, QIN S J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis[J]. IEEE Transactions on semiconductor manufacturing, 2006, 19(2): 159-172. doi: 10.1109/TSM.2006.873524http://doi.org/10.1109/TSM.2006.873524.

WANG X, KRUGER U, IRWIN G W, et al. Nonlinear PCA with the local approach for diesel engine fault detection and diagnosis[J]. IEEE Transactions on Control Systems Technology, 2007, 16(1): 122-129. doi: 10.1109/TCST.2007.899744http://doi.org/10.1109/TCST.2007.899744.

LU B, ZHAO Y, MAO Z. Fault diagnosis method based on moving window PCA[C]. 2009 Chinese Control and Decision Conference. IEEE, 2009: 185-188. doi: 10.1109/CCDC.2009.5195109http://doi.org/10.1109/CCDC.2009.5195109.

YU H, KHAN F, GARANIYA V. A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes[J]. AIChE Journal, 2016, 62(5): 1494-1513. doi: 10.1002/aic.15136http://doi.org/10.1002/aic.15136.

GODOY J L, VEGA J R, MARCHETTI J L. A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space[J]. Chemometrics and Intelligent Laboratory Systems, 2013, 128: 25-36. doi: 10.1016/j.chemolab.2013.07.006http://doi.org/10.1016/j.chemolab.2013.07.006.

KRESTA J V, MACGREGOR J F, MARLIN T E. Multivariate statistical monitoring of process operating performance[J]. The Canadian journal of chemical engineering, 1991, 69(1): 35-47. doi: 10.1002/cjce.5450690105http://doi.org/10.1002/cjce.5450690105.

MACGREGOR J F, JAECKLE C, KIPARISSIDES C, et al. Process monitoring and diagnosis by multiblock PLS methods[J]. AIChE Journal, 1994, 40(5): 826-838. doi: 10.1002/aic.690400509http://doi.org/10.1002/aic.690400509.

QIN S J, MCAVOY T J. Nonlinear PLS modeling using neural networks[J]. Computers & Chemical Engineering, 1992, 16(4): 379-391. doi: 10.1016/0098-1354(92)80055-Ehttp://doi.org/10.1016/0098-1354(92)80055-E.

何宁. 基于ICA-PCA方法的流程工业过程监控与故障诊断研究[D]. 浙江大学, 2004.

KANO M, TANAKA S, HASEBE S, et al. Monitoring independent components for fault detection[J]. AIChE Journal, 2003, 49(4): 969-976. doi: 10.1002/aic.690490414http://doi.org/10.1002/aic.690490414.

ZHANG Y, QIN S J. Fault detection of nonlinear processes using multiway kernel independent component analysis[J]. Industrial & engineering chemistry research, 2007, 46(23): 7780-7787. doi: 10.1021/ie070381qhttp://doi.org/10.1021/ie070381q.

YOO C K, LEE J M, VANROLLEGHEM P A, et al. On-line monitoring of batch processes using multiway independent component analysis[J]. Chemometrics and intelligent laboratory systems, 2004, 71(2): 151-163.

GE Z, SONG Z. Process monitoring based on independent component analysis-principal component analysis(ICA-PCA) and similarity factors[J]. Industrial & Engineering Chemistry Research, 2007, 46(7): 2054-2063. doi: 10.1021/ie061083ghttp://doi.org/10.1021/ie061083g.

GUO M, XIE L, WANG S Q, et al. Research on an integrated ICA-SVM based framework for fault diagnosis[C]. SMC′03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483). IEEE, 2003, 3: 2710-2715.

RYCHETSKY M, ORTMANN S, GLESNER M. Support vector approaches for engine knock detection[C]. IJCNN′99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339). IEEE, 1999, 2: 969-974.

JACK L B, NANDI A K. Support vector machines for detection and characterization of rolling element bearing faults[J]. Journal of Mechanical Engineering Science, 2001: 1065-1071.

GE M, et al. Fault diagnosis using support vector machine with an application in sheet metal stamping operations[J]. Mechanical Systems and Signal Processing, 2004. 18(1): 143-159. doi: 10.1016/S0888-3270(03)00071-2http://doi.org/10.1016/S0888-3270(03)00071-2.

ANTONELLI G, CACCAVALE F, SANSONE C, et al. Fault diagnosis for AUVs using support vector machines[C]. IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA′04. 2004. IEEE, 2004, 5: 4486-4491.

SHINA H J, EOMB D H, KIM S S. One-class support vector machines—: an application in machine fault detection and classification[J]. Computers & Industrial Engineering, 2005. 48: 395-408.

WIDODO A, YANG B. Wavelet support vector machine for induction machine fault diagnosis based on transient current signal[J]. Expert Systems with Applications, 2008. 35(1): 307-316. doi: 10.1016/j.eswa.2007.06.018http://doi.org/10.1016/j.eswa.2007.06.018.

HOPFIELD J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the national academy of sciences, 1982. 79(8): 2554-2558.

KOHONEN T. Self-organization and associative memory[M]. Springer Science & Business Media, 2012.

ACKLEY D H, HINTON G E, SEJNOWSKI T J. A learning algorithm for Boltzmann machines[J]. Cognitive science, 1985, 9(1): 147-169.

RUMELHART D E, HINTON G E, WILLIAMS R J. Learning internal representation by error propagation[J]. Neurocomputing: foundations of research, 1988: 533-536.

CHOW M, MANGUM P, THOMAS R J. Incipient fault detection in DC machines using a neural network[C]. Twenty-Second Asilomar Conference on Signals, Systems and Computers. IEEE, 1988, 2: 706-709.

DIETZ W E, KIECH E L, ALI M. Pattern-based fault diagnosis using neural networks[C]. Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems-Volume 1. 1988: 13-23. doi: 10.1145/51909.51911http://doi.org/10.1145/51909.51911.

VENKATASUBRAMANIAN V, CHAN K. A neural network methodology for process fault diagnosis[J]. AIChE Journal, 1989, 35(12):1993-2002.

CHEN Z Q, LI C, SANCHEZ R V. Gearbox fault identification and classification with convolutional neural networks[J]. Shock and Vibration, 2015. 2015(2): 1-10.

FAN J Y, NIKOLAOU M, WHITE R E. An approach to fault diagnosis of chemical processes via neural networks[J]. AIChE journal, 1993, 39(1):82-88.

FARELL A E, ROAT S D. Framework for enhancing fault diagnosis capabilities of artificial neural networks[J]. Computers\& chemical engineering, 1994, 18(7):613-635.

TSAI C S, CHANG C T. Dynamic process diagnosis via integrated neural networks[J]. Computers & chemical engineering, 1995, 19S:S747-S752.

NARENDRA K G, SOOD V K, KHORASANI K, et al. Application of a radial basis function(RBF) neural network for fault diagnosis in a HVDC system[J]. Power Systems, IEEE Transactions on, 1998, 13(1):177-183.

YU D L, GOMM J B, WILLIAMS D. Sensor fault diagnosis in a chemical process via RBF neural networks[J]. Control Engineering Practice, 1999, 7(1):49-55.

FRANK P M, KOPPEN-SELIGER B. Fuzzy logic and neural network applications to fault diagnosis[J]. International Journal of Approximate Reasoning, 1997. 16(1): 67-88.

QUTEISHAT A, LIM C P. A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification[J]. Applied soft computing, 2008. 8(2): 985-995. doi: 10.1016/j.asoc.2007.07.013http://doi.org/10.1016/j.asoc.2007.07.013.

QIN X, HAN B, CUI L. A kind integrated adaptive fuzzy neural network tolerance analog circuit fault diagnosis method[C]. 2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering. IEEE, 2011, 1: 180-183. doi: 10.1109/CCIENG.2011.6007987http://doi.org/10.1109/CCIENG.2011.6007987.

CHEN J C. ROBERTS C, WESTON P. Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems[J]. Control Engineering Practice, 2008. 16(5): 585-596.

LING W, JIA M, XU F, et al. Optimizing strategy on rough set neural network fault diagnosis system[J]. Proceedings of the Csee, 2003, 5, 98-102.

DONG L, XIAO D, LIANG Y, et al. Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers[J]. electric power systems research, 2008, 78(1):129-136.

THUKARAM D, KHINCHA H P, VIJAYNARASIMHA H P. Artificial neural network and support vector machine approach for locating faults in radial distribution systems[J]. IEEE Transactions on Power Delivery, 2005, 20(2Part 1):710-721.

TANG C, HE Y, YUAN L. A Fault Diagnosis Method of Switch Current Based on Genetic Algorithm to Optimize the BP Neural Network[J]. Electrical Power Systems and Computers, 2011:943-950. doi: 10.1007/978-3-642-21747-0_122http://doi.org/10.1007/978-3-642-21747-0_122.

WU J D, LIU C H. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network[J]. Expert systems with applications, 2009, 36(3):4278-4286.

WANG C C, KANG Y, SHEN P C, et al. Applications of fault diagnosis in rotating machinery by using time series analysis with neural network Gear fault diagnosis based on the improved wavelet neural network and simulation[J]. Expert Systems with Applications, 2010, 37(2):1696-1702.

WU J D, CHIANG P H, CHANG Y W, et al. An expert system for fault diagnosis in internal combustion engines using probability neural network[J]. Expert Systems with Applications, 2008, 34(4):2704-2713.

RAJAKARUNAKARAN S, VENKUMAR P, DEVARAJ D, et al. Artificial neural network approach for fault detection in rotary system[J]. Applied Soft Computing, 2008. 8(1):740-748.

CHENGCAI M, XIAODONG G, YUANYUAN W. Fault diagnosis of power electronic system based on fault gradation and neural network group[J]. Neurocomputing, 2009. 72(13): 2909-2914. doi: 10.1016/j.neucom.2008.06.033http://doi.org/10.1016/j.neucom.2008.06.033.

ZHANG J. Improved on-line process fault diagnosis through information fusion in multiple neural networks[J]. Computers & chemical engineering, 2006. 30(3): 558-571.

TAMILSELVAN P, WANG P. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety, 2013, 115: 124-135.

SUN W, SHAO S, ZHAO R, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification[J]. Measurement, 2016, 89: 171-178. doi: 10.1016/j.measurement.2016.04.007http://doi.org/10.1016/j.measurement.2016.04.007.

PROKHOROV D V. Toyota Prius HEV neurocontrol and diagnostics[J]. Neural Networks, 2008, 21(2): 458-465.

王旭红, 何怡刚. 基于对角递归神经网络的异步电动机定子绕组匝间故障诊断方法[J]. 电力自动化设备, 2009 (7): 60-63.

SHI L, WANG L, WANG Z. The Modeling and the Sensor Fault Diagnosis of a Continuous Stirred Tank Reactor with a Takagi-Sugeno Recurrent Fuzzy Neural Network[J]. International Journal of Distributed Sensor Networks, 2009, 5(1): 37-37.

FAHLMAN S E, LEBIERE C.The Cascade-Correlation learning architecture[J/OL]. 1993: 831562 Bytes. doi: 10.1184/R1/6610403.v1http://doi.org/10.1184/R1/6610403.v1.

HOEHFELD M, FAHLMAN S E. Learning with limited numerical precision using the cascade-correlation algorithm[J/OL]. IEEE Transactions on Neural Networks, 1992, 3(4): 602-611. doi: 10.1109/72.143374http://doi.org/10.1109/72.143374.

WILAMOWSKI B M, HAO YU. Neural Network Learning Without Backpropagation[J/OL]. IEEE Transactions on Neural Networks, 2010, 21(11): 1793-1803. doi: 10.1109/TNN.2010.2073482http://doi.org/10.1109/TNN.2010.2073482.

ZHANG L, LI K, BAI E W. A New Extension of Newton Algorithm for Nonlinear System Modelling Using RBF Neural Networks[J/OL]. IEEE Transactions on Automatic Control, 2013, 58(11): 2929-2933. doi: 10.1109/TAC.2013.2258782http://doi.org/10.1109/TAC.2013.2258782.

G.-B. HUANG, SARATCHANDRAN P, SUNDARARAJAN N. A Generalized Growing and Pruning RBF(GGAP-RBF) Neural Network for Function Approximation[J/OL]. IEEE Transactions on Neural Networks, 2005, 16(1): 57-67. doi: 10.1109/TNN.2004.836241http://doi.org/10.1109/TNN.2004.836241.

HAN S, MAO H, DALLY W J. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding[J/OL]. 2015. doi: 10.48550/arXiv.1510.00149http://doi.org/10.48550/arXiv.1510.00149.

CHEN T, GOODFELLOW I, SHLENS J. Net2Net: Accelerating Learning via Knowledge Transfer[J/OL]. 2015. doi: 10.48550/arXiv.1511.05641http://doi.org/10.48550/arXiv.1511.05641.

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