Review of research on intelligent diagnosis of oil transfer pump malfunction

Liangliang Dong , Qian Xiao , Yanjie Jia , Tianhai Fang

Petroleum ›› 2023, Vol. 9 ›› Issue (2) : 135 -142.

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Petroleum ›› 2023, Vol. 9 ›› Issue (2) :135 -142. DOI: 10.1016/j.petlm.2022.01.002
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Review of research on intelligent diagnosis of oil transfer pump malfunction
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Abstract

Oil transfer pump is the key dynamic equipment in the process of oil and gas gathering and transportation, and its working reliability directly affects the safety of oil and gas storage and transportation. Intelligent diagnosis is a key technical method to reduce failure rate of oil transfer pump, ensure the safety of gathering and transportation process, and avoid major safety accidents caused by oil transfer pump failure. Various oil transfer pumps have been emerged in recent decades, and the common fault types and characteristics of oil transfer pump have been brought out in the review. This article highlights on the research of the fault signal and processing methods of oil transfer pump. Firstly, the fault signal of the oil transfer pump is discussed and the advantages and disadvantages of different signal extraction are analyzed. Secondly, the intelligent diagnosis method of oil transfer pump and the shortcomings of the existing methods are pointed out. Finally, the conclusions are given and the future development perspectives of oil transfer pumps are suggested. The main contribution of this review is to give a syn-thetic understanding on oil transfer pumps.

Keywords

Intelligent diagnosis / Oil transfer pump / Development trend

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Liangliang Dong, Qian Xiao, Yanjie Jia, Tianhai Fang. Review of research on intelligent diagnosis of oil transfer pump malfunction. Petroleum, 2023, 9(2): 135-142 DOI:10.1016/j.petlm.2022.01.002

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Acknowledgments

This study is supported by Sichuan science and technology program (2021ZHCG0013, 22ZDYF3009), China National Petroleum Corporation science and technology planning project(2020B-4121). Such supports are greatly appreciated by the authors.

References

[1]

Xiang Wang, Xiaozheng Yang, Weiguang Zhang,Q/SY GD 0028—2011 Operation, Maintenance and Repair Rules for Centrifugal Oil Pump units[S], Petroleum Industry Press, Beijing, 2011.

[2]

O. Hamomd, S. Alabied, Y. Xu, et al., Vibration Based Centrifugal Pump Fault Diagnosis Based on Modulation Signal Bispectrum analysis[C]. 2017 23rd International Conference on Automation and Computing(ICAC), 2017, pp. 1-5. Huddersfieid.

[3]

M.N. Nazarova, A.G. Palaev, Diagnostics and repair of centrifugal oil transfer pump rotor shaft[J], Earth Environ. 87 (9) (2017) 1-6.

[4]

Samir Alabied, Alsadak Daraz, Khalid Rabeyee, et al., Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis[C], 2019, pp. 1-7. Proceeding of the 25th International Conference on Automation & Computing.

[5]

Yang Wang, Hongyong Fu, Ke Wang, et al., A Fault Diagnosis Method Based on Multi-Scale Entropy for Centrifugal Pump[C], 2018, pp. 974-978, 2018 Prognostics and System Health Management Conference (PHM-Chongqing).

[6]

Long Peng, Guoqing Han, Arnold Landjobo Pagou, et al., Electric submersible pump broken shaft fault diagnosis based on principal component analysis[J], J. Petrol. Sci. Eng. 191 (3) (2020) 1-6.

[7]

Jinguo Sang, Research on Pump Fault Diagnosis Based on Pso-Bp Neural Network algorithm[C], 2019, pp. 1748-1752, 2019IEEE 8th Joint International Technology and Artificial Intelligence Conference (ITAIC).

[8]

Chaowei Wang, Li Peng, Boren Chen, et al., Design of Pump Fault Diagnosis System Based on T-FMEA[C], 2018, pp. 1-6, 2018 2nd International Conference on Data Mining,Communications and Information Technology (DMCIT).

[9]

H. Yang. Research on RCM-based Maintenance Strategy of Mobile Equipment of Offshore Oil and Gas Production Platform[D], Southwest Petroleum University, 2017.

[10]

J. Lee, Approaching Zero downtime[R], The Center for Intelligent Maintenance Systems, Harbor Research Pervasive Internet Report, 2003.

[11]

S. Vohnout, M. Engelman, E. Enikov, Miniature MEMS-based data recorder for prognostics and health management[J], IEEE Instrum. Meas. Mag. 14 (4) (2011) 18-26.

[12]

Guang Kang, Xiaodong Chen, Research on Equipment Support under the Conditions of informatization[M], People's Liberation Army Press, Beijing, 2011, pp. 303-307.

[13]

Lei Tian, Junhui Liu, Xiangdong Sun, et al., Application of the state monitoring and fault diagnosis system in pump units[J], Contemp. Chem. Ind. 44 (3) (2015) 567-569+576.

[14]

J.K. Shina, K. Elbhbah, A future possibility of vibration based condition monitoring of rotating machines[J], Mech. Syst. Signal Process. 34 (1) (2013) 231-240.

[15]

Minping Jia, Guixing Liu, Theory and application of sensor placement in condition monitoring[J], J. Southeast Univ. (Nat. Sci. Ed.) 41 (1) (2011) 77-81.

[16]

Xianglou Liu, Jicheng Chang, Ruinan Liu, et al., Noise sample synthesis of early state oil pump cavitation based on characteristic frequency analysis[J], Autom. Instrument. (12) (2017) 162-164.

[17]

U. Galarza, G. Rubio, et al., Predictive maintenance of wind turbine low-speed shafts based on an autonomous ultrasonic system[J], Eng. Fail. Anal. 103 (9) (2019) 481-504.

[18]

Yunlong Zhou, Yuanzheng Lv, Incipient cavitations fault diagnosis for a centrifugal pump based on multi-position noise analysis[J], J. Vib. Shock 36 (7) (2017) 39-44.

[19]

Shutao Zhao, Erxu Wang, Xiuxin Chen, et al., Fault diagnosis method for large motor based on sound-vibration signal combined with 1D-CNN[J], J. Harbin Inst. Technol. 52 (9) (2020) 116-122.

[20]

Hunju Liu, Shiping Xu, Predictive maintenance method based on oil analysis condition monitoring[J], Mining Process. Equip. (6) (1996), 45-47+3-4.

[21]

Yu Fan, Predictive maintenance of equipment based on infrared thermal imaging detection technology[J], Plant Mainten. Eng. (2) (2017) 36-38.

[22]

S. Yan, B. Ma, et al., A unified system residual life prediction method based on selected tribodiagnostic data[J], IEEE Access (7) (2019) 44087-44096.

[23]

Shunming Li, Haidong Guo, Dianrong Li, Review of vibration signal processing methods[J], Chin. J. Sci. Instrum. (8) (2013) 1907-1915.

[24]

Qingfeng Guo, Chengdong Wang, Peisen Liu, Application of time domain index and kurtosis analysis method in the fault diagnosis of rolling bearing[J], J. Mech. Transm. 40 (11) (2016) 172-175.

[25]

Bingxi Zhao, Dawei Ji, Yuan Qi, et al., rubbing fault diagnosis of rotor system based on combined feature space in time and time-frequency domains[J], J. Xi'an Jiaot. Univ. 54 (1) (2020) 75-84.

[26]

Yuxue Fan, Jiangwen Wang, Guiming Mei, et al., Rolling bearing fault diagnosis method based on BI-LSTM under less samples condition[J], Noise Vib. Control 40 (4) (2020) 103-108.

[27]

Zhenya Quan, Qian Zhang, Xiaoxia Shi, fault diagnosis method based on ITD and short-time fourier transform[J], Equip. Manufac. Technol. (12) (2018) 221-224.

[28]

Rong Xing, Bingpeng Gao, Peihao Hou, et al., Research of fault diagnosis of rolling bearing based on MSCNN and STFT[J], J. Mech. Transm. 44 (7) (2020) 41-45+58.

[29]

Xiaoyan Zhu, Yongjie Wang, A method of incipient fault diagnosis of bearings based on autocorrelation analysis and MCKD[J], J. Vib. Shock 39 (2) (2019) 396-403.

[30]

Jiancheng Yin, Mingqiang Xu, Damage degree recognition of bearing based on correlation analysis and Lempel-Ziv index[J], J. Vib. Meas. Diagnosis 39 (2) (2019) 396-403.

[31]

Hao Guocheng, Fan Tan, Zhuo Cheng, et al., Time-frequency analysis of BGabor-NSPWVD algorithm with strong robustness and high sharpening concentration[J], Acta Autom. Sin. (3) (2019) 566-576.

[32]

Guiji Tang, Bin Pang, Yuling He, Rotating machinery fault diagnosis method based on ITD and Wigner-Ville distribution[J], Chin. Measur. Test 41 (1) (2015) 85-88.

[33]

Pengfei Wang, Yijia Zhao, Baoliang Xiong, et al., Analysis of fault prediction and diagnosis of rotating machinery[J], Electric Switchgear (3) (2020) 23-29.

[34]

Peiming Shi, Xiaoci Guo, Dongying Han, et al., A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis[J], J. Mech. Sci. Technol. 34 (4) (2020) 1445-1458.

[35]

P. Singru, V. Krishnakumar, D. Natarajan, et al., Bearing failure prediction using Wigner-Ville distribution, modified Poincare mapping and fast Fourier transform[J], J. Vibroeng. 20 (1) (2018) 127-137.

[36]

Zhihao Jin, Baogang Yu, Zhengxin Yang, et al., The rub-impact fault diagnosis based on wavelet analysis-least square support vector machine[J], J. Shenyang Univ. Chem. Technol. 33 (3) (2019) 251-256.

[37]

Hongyan Zuo, Xiaobo Liu, Lianhuan Hong, Improved wavelet clustering algorithm for rotor fault diagnosis[J], J. Vib. Meas. Diagnosis (2) (2018) 320-326.

[38]

Kou Lin, Chenyuan Ma, Zhenyu Huang, et al., HHT spectrum analysis method for identification of small cracks in rotor-bearing system[J], Power Syst. Clean Energy 36 (6) (2020) 45-53.

[39]

Ze Hu, Zhibo Zhang, Xiaojie Wang, et al., fault diagnosis of rolling bearing based on Hilbert Huang transform and neural network[J], Electric Tool (1) (2020) 11-18.

[40]

F. Sabbaghianbidgoli, J. Poshtan, Fault detection of broken rotor bar using an improved form of Hilbert-Huang transform[J], Fluctuation Noise Lett. 17 (4) (2018) 185-197.

[41]

Erivan Francisco, et al., Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques[J], Energy 171 (2019) 556-565.

[42]

G.Z. Liu, J.M. Zhao, et al., Bearing degradation trend prediction under different operational conditions based on CNN-LSTM[J], Mater. Sci. Eng. 612 (3) (2019) 1-6.

[43]

B. Zhang, S. Zhang, et al., Bearing performance degradation assessment using long short-term memory recurrent network[J], Comput. Ind. 106 (4) (2019) 14-29.

[44]

M.A. Farsi, S.M. Hosseini, Statistical distributions comparison for remaining useful life prediction of components via ANN[J], Int. J. Syst. Assur. Eng. Manag. 10 (3) (2019) 429-436.

[45]

M. Sadoughi, C. Hu, Physics-based convolutional neural network for fault diagnosis of rolling element bearings[J], IEEE Sensor. J. 19 (11) (2019) 4181-4192.

[46]

Juntai Xie, Xiaozhe Lv, Jianmin Gao, et al., SOM-based dynamic tagging method of operation process for complex electromechanical system[J], J. Vib. Meas. Diagnosis 40 (2) (2020) 341-347.

[47]

Xiaofeng Li, Tancheng Xie, Yanwei Xu, et al., The research on the intelligent diagnosis expert system for bearing fault[J], Manuf. Autom. 42 (1) (2020) 7-9.

[48]

F. Cheng, L. Qu, W. Qiao, et al., Enhanced particle filtering for bearing remaining useful life prediction of wind turbine drivetrain gearboxes[J], IEEE Trans. Ind. Electron. 66 (6) (2019) 4738-4748.

[49]

Zhuqing Bi, Chenming Li, Xujie Li, et al., Research on fault diagnosis for pumping station based on T-S fuzzy fault tree and bayesian network[J], J. Electrical Comput. Eng. (2017) 1-7.

[50]

A. Vinaya, Q. Arifianti, N. Yessica, et al., Fault Diagnosis of Water Pump Based on Acoustic Emission Signal Using Fast Fourier Transform Technique and Fuzzy Logic Inference[C], 2019, pp. 1-6, 2019 International Conference on Engineering, Science, and Industrial Applications (ICESI).

[51]

Naiquang Su, Li Xiao, Qinghua Zhang, et al., Composite fault diagnosis for rotating machinery of large units based on evidence theory and multiinformation fusion[J], Shock Vib. (2) (2019) 1-13.

[52]

Zhiqiang Meng, Shaojiang Dong, Xuejiao Pan, et al., Study on life state identification of bolling bearing based on information fusion[J], Modular Mach. Tool Automat. Manufac. Tech. 3 (3) (2020) 41-44.

[53]

Peng Liu, Tao Liu, Sihong Wang, et al., Bearing fault diagnosis method based on information fusion and fast ICA[J], J. Vib. Shock 39 (3) (2020) 250-259.

[54]

Yajuan Gao, Chen Lei, Huiyi Lin, et al., Research on rolling bearing fault prediction based on full vector KPCA and AR model[J], Mach. Design Manufac. 11 (11) (2019) 20-24.

[55]

Chao Yang, Xiaoxia Yang, Early fault diagnosis of rolling bearing based on GRD and TEO[J], J. Vib. Shock 39 (13) (2020) 224-229.

[56]

Pinghua Ju, Ke Lei, Ran Yan, et al., Failure probability prediction method on parts of generalized regression neural network based on GRA and AHP[J], J. Hunan Univ. (Natural Sciences) 46 (4) (2019) 34-40.

[57]

Fei Guan, Weiwei Cui, Lianfeng Li, et al., A comprehensive evaluation method of sensor selection for PHM based on grey clustering[J], Sensors 20 (6) (2020) 1710-1724.

[58]

A. Fentaye, G. Ui-Haq, et al., Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method[J], J. Power Energy 233 (9) (2019) 786-802.

[59]

G. Deepam, C. Anurag, B.S. Pabla, et al., Support vector machines based noncontact fault diagnosis system for bearings[J], J. Intell. Manuf. 31 (2020) 1275-1289.

[60]

Yan Xue, Dongyang Dou, Jiangguo Yang, Multi-fault diagnosis of rotating machinery based on deep convolution netural network and support vector machine[J], Measurement (2020) 156.

[61]

International Electrotechnical Commission60300-3-11-2002. Reliability Management. Part 3-11: Application Guide. Maintenance around reliability.

[62]

P.D. Gabriel, F.V. Alexandru, et al., Research on the reliability and maintenance of pumps from the process flow of coal preparations of the energy complex hunedoara[J], Int. Multidiscip. Sci. Geoconf. (2015) 697-704.

[63]

J. Liniger, M. Soltani, et al., Feasibility Study of a Simulation Driven Approach for Estimating Reliability of Wind Turbine Fluid Power Pitch systems[C], 2018, pp. 2037e 2044. Proceedings of the 28th International Europaen Safety and Reliability Conference.

[64]

Z. Vintr, M. Vintr, Tools for Components Reliability prediction[C], 2017, pp. 2187e 2194. Safety and Reliability-Theory and Applications-Proceedings of the 27th European Safety and Reliability Conference.

[65]

P. Binh, Q.T. Huynh, et al., Applying PNZ model in reliability prediction of component-based systems and fault tolerance structures technique[J], Social-Inform. Telecommun. Eng. 165 (2016) 272-281.

[66]

T. Hayat, H. Seifzadeh, A New Approach to Reliability Prediction in Componentbased systems[C], 2017, pp. 89-94. Proceeding of the 8th International Conference on Computer Modeling and Simulation.

[67]

M. Franko, B. Pani, et al., Damage Based Reliability Prediction of Dynamically Loaded components[C], 2017, pp. 2053e 2058. Safety and Reliability-Theory and Applications-Proceedings of the 27th European Safety and Reliability Conference.

[68]

R. Guo, C. Ning, et al., Life prediction and test period optimization reaearch based on small sample reliability test of hydraulic pumps[J], High Technol. Lett. 23 (1) (2017) 63-70.

[69]

K. Verbert, B. De Schutter, et al., A multiple-model reliability prediction approach for condition-based maintenance[J], IEEE Trans. Reliab. 67 (3) (2018) 1364-1376.

[70]

I. Vernica, H. Wang, et al., Uncertainty analysis of capacitor reliability prediction due to uneven thermal loading in photovoltaic applications[J], Microelectron. Reliab. 88 (2018) 1036-1041.

[71]

G.Q. Fang, Rong Pan, et al., A Copula-Based Multivariate Degradation Analysis for Reliability prediction[C], 2018, pp. 1-7, 2018 Annual Reliability and Maintainability Symposium (RAMS).

[72]

M. Sun, B. Jing, et al., Research on Life Prediction of Airborne Fuel Pump Based on Combination of Degradation Data and Life data[C], 2018, pp. 664-668, 2018 Prognostics and System Health Management Conference.

[73]

D. Straub, V. Malioka, M.H. Faber, A framework for the asset integrity management of large deteriorating concrete structures[J], Struct. Infrastruct. Eng. 5 (2009) 199-213.

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