Magnetic pumps transmit torque without leaks using magnetic induction, offering superior sealing over conventional pumps by replacing dynamic with static seals. They are vital for handling hazardous or valuable fluids, enhancing safety and efficiency while cutting maintenance costs. Despite their reliability, they face failure risks from temperature, lubrication, and contamination. This article reviews fault patterns, causes, and impacts in magnetic pumps and explores advanced sensor-based and intelligent diagnostic methods to predict and prevent failures, ensuring stable operation and reducing downtime. It provides a comprehensive overview of current fault diagnosis techniques and future research directions in the field.
YuanDQHeYYChenXY, et al. Research status and development prospect of magnetic pump. J Magn Mater Dev2011; 42(2): 1–8.
2.
WangYLDengWJ.The past, present and future of the magnetic drive pumps. Journal of Magnetic Materials and Dev2013; 44(6): 70–75.
3.
SuHLeeJ.Machine learning approaches for diagnostics and prognostics of industrial systems using open source data from PHM data challenges: a review. Comput Sci Mach Learn2023; 2312: 16810.
4.
GuanXF.Modern pumps theory and design. Beijing: China Aerospace Publishing House, 2011, pp. 736–738.
5.
MaJZhuZQ.Magnet eddy current loss reduction in permanent magnet machines. IEEE Trans Ind Appl2019; 55(2): 1309–1320.
6.
GaoZHuC.Magnet eddy current loss reduction in permanent magnet machines. IEEE Access2019; 7: 100003–100013.
7.
YuanYYuanSTangL.Numerical investigation on the mechanism of double-volute balancing radial hydraulic force on the centrifugal pump. Processes2019; 7(10): 689.
8.
SunJQuXZhouY, et al. Flow field simulation and axial thrust analysis of high temperature and pressure magnetic drive pump. J Drain Irrig Mach Eng2019; 37(1): 25–30.
9.
KimW-SYunJ-E.CFD analysis on the balancing hole design for magnetic drive centrifugal pumps. Energies2020; 13(22): 5865.
10.
KimSHShinJWIshiyamaK.Magnetic bearings and synchronous magnetic axial coupling for the enhancement of the driving performance of magnetic wireless pumps. IEEE Trans Magn2014; 50(1): 1–4.
11.
SeoS-WKimY-HLeeJ-H, et al. Analytical torque calculation and experimental verification of synchronous permanent magnet couplings with Halbach arrays. AIP Adv2017; 8(5): 056609.
12.
LiKBirdJZAcharyaVM, et al. Ideal radial permanent magnet coupling torque density analysis. IEEE Trans Magn2017; 53(6): 8106304.
13.
PernaSSchreflTSerpicoC, et al. Microstructure role in permanent magnet eddy current losses. IEEE Trans Magn2021; 57(2): 1–5.
14.
JiaqiongWGuotaoJKaiW, et al. Stress and deformation analysis of magnetic coupling based on multi-field coupling of magnetic-fluid-thermal-solid. J Drain Irrig Mach Eng2022; 40(5): 475–481.
15.
GaoZZhangJLiW, et al. The flow-heat coupling study of cooling circulating channel of magnetic drive pump. Mechanics2021; 27(4): 285–294.
16.
GangH.Problems analysis and solutions of magnetic pump used in styrene unit. Guangzhou Chem Indust2019; 47(23): 135–137.
17.
XiangZSiweiZKaiW.The flow-heat coupling study of cooling circulating channel of magnetic drive pump. Water2023; 15(23): 4060.
18.
ChalghoumIElaoudSKanfoudiH, et al. The effects of the rotor-stator interaction on unsteady pressure pulsation and radial force in a centrifugal pump. J Hydrodyn2018; 30(4): 672–681.
19.
BurgerNDLGhelaPNMulderAO, et al. Tribological failure analysis of a magnetic drive pump. Eng Fail Anal2009; 16(5): 1501–1509.
20.
PeiZHaoZDe-FuX, et al. Design of sliding bearing wear monitoring sensor in magnetic drive pumps. J Drain Irrig Mach Eng2019; 37(5): 400–405.
21.
GaoZHuCLiuJ, et al. The study on pressure pulsation of cooling circulating channel of magnetic drive pump. J Vibroeng2019; 21(5): 1456–1471.
22.
ZhangH-LKongF-YZhuA-X, et al. Effect of blade outlet angle on radial force of marine magnetic drive pump. Shock Vib2020; 2020: 1–18.
23.
RapurJSTiwariR.Automation of multi-fault diagnosing of centrifugal pumps using multi-class support vector machine with vibration and motor current signals in frequency domain. J Braz Soc Mech Sci Eng2018; 40(6): 278.
24.
TianXFengGChenbZ, et al. The investigation of motor current signals from a centrifugal pump for fault diagnosis. In: Proceedings of the 27th international congress of condition monitoring and diagnostic engineering, Brisbane, Australia, 16–18 September. COMADEM, 2014, pp. 1–6.
25.
KumarDDewanganATiwariR, et al. Identification of inlet pipe blockage level in centrifugal pump over a range of speeds by deep learning algorithm using multi-source data. Measurement2021; 186: 110146.
26.
YuRLiuJ.Failure analysis of centrifugal pump impeller. Eng Fail Anal2018; 92: 343–349.
27.
SunHSiQChenN, et al. HHT-based feature extraction of pump operation instability under cavitation conditions through motor current signal analysis. Mech Syst Signal Process2020; 139: 106613.
28.
SunHYuanSLuoY.Cyclic spectral analysis of vibration signals for centrifugal pump fault characterization. IEEE Sens J2018; 18(7): 2925–2933.
29.
ManikandanSDuraiveluK.Vibration-based fault diagnosis of broken impeller and mechanical seal failure in industrial mono-block centrifugal pumps using deep convolutional neural network. J Vib Eng Technol2022; 11(1): 141–152.
30.
TongZXinJTongS, et al. Internal flow structure, fault detection, and performance optimization of centrifugal pumps. J Zhejiang Univ Sci A2020; 21(2): 85–117.
31.
KongFShenX.Performance study based on inner flow field numerical simulation of magnetic drive pumps with different rotate speeds. Chin J Mech Eng2012; 1(25): 137–143.
32.
KumarPHatiAS.Review on machine learning algorithm based fault detection in induction motors. Arch Comput Method Eng2020; 28(3): 1929–1940.
33.
NakamuraH.A diagnosis method of bearing and stator fault in motor using rotating sound based on deep learning. Energies2021; 14(5): 1319.
34.
SunalCEDyoVVelisavljevicV, et al. Review of machine learning based faultdetection for centrifugal pump induction motors. IEEE Access2022; 10: 71344–71335.
35.
SharifiREbrahimiM.Detection of stator winding faults in induction motors using three-phase current monitoring. ISA Trans2011; 50(1): 14–20.
36.
FilhoPnascimentoNMRocha de SousaI, et al. A reliable approach for detection of incipient faults of short-circuits in induction generators using machine learning. Comput Electr Eng2018; 50(1): 14–20.
37.
LSTM, Nicodjimenez/lstm: Minimal, clean example of lstm neural network training in python, for learning purposes.
38.
IrhoumahMPuscaRLefevreE, et al. A diagnosis detection of the stator winding inter-turn faults in induction and synchronous machines through the correlation between harmonics of the voltage of two magnetic flux sensors. Magnet Eddy Current Loss Reduction in Permanent Magnet Machines2019; 55(3): 2682–2689.
39.
RF, zhaoxingfeng/RandomForest: Random Forest(RF)
40.
CNN, dennybritz/cnn-text-classification-tf: Convolutional Neural Network for Text Classification in Tensorflow
41.
RaiAUpadhyaySH.A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol Int2016; 96: 289–306.
42.
JinXMingboZChowTWS, et al. Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Trans Ind Electron2014; 61(5): 2441–2451.
43.
LiZWangYMaJ.Fault diagnosis of motor bearings based on a convolutional long short-term memory network of Bayesian optimization. IEEE Access2021; 9: 97546–97556.
44.
TomaRNProsvirinAEKimJ-M.Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors2020; 20(7): 1884.
45.
WuYShuqionSAnQ, et al. Treatment strategy research on a squirrel-cage induction motor with broken rotor bar faults. Sensors2022; 22(12): 4345.
46.
Abd-el-MalekMBAbdelsalamAKHassanOE.Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor. Sensors2018; 80: 439–457.
47.
XuBWangZSunL. Detection of rotor broken bar fault of asynchronous motor based on Duffing and PSO algorithm. In: 20th International conference on electrical machines and systems, Sydney, Australia, 11–14 August. Piscataway, N.J.: IEEE. 2017, pp. 1–5.
48.
LiuRYangBZioE, et al. Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Process2018; 108: 33–47.
49.
TidririKChattiNVerronS, et al. Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges. Ann Rev Control2016; 42: 63–81.
50.
AhmadZRaiAMaliukAS, et al. Discriminant feature extraction for centrifugal pump fault diagnosis. IEEE Access2020; 8: 165512–165528.
51.
SoualhiAMedjaherKZerhouniN.Bearing health monitoring based on hilbert–huang transform, support vector machine, and regression. IEEE Trans Instrum Meas2015; 64(1): 52–62.
52.
YariBSafizadehMS.Pump cavitation detection through fusion of support vector machine classifier data associated with vibration and motor current signature. Insight Non-Destr Test Cond Monit2017; 59(12): 669–673.
53.
ShifatATHurJ-W. EEMD assisted supervised learning for the fault diagnosis of BLDC motor using vibration signal. J Mech Sci Technol2020; 34(10): 3981–3990.
54.
ALTobiMASBevanGWallaceP, et al. Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT. Eng Sci Technol2019; 22(3): 854–861.
55.
ZhuRLiYHuangQ, et al. Research on vertical sec centrifugal pump multi-fault diagnosis based on WPT–SVM. Energies2023; 16(22): 7653.
56.
ZamanWAhmadZSiddiqueMF, et al. Centrifugal pump fault diagnosis based on a novel sobeledge scalogram and CNN. Sensors2023; 23(11): 5255.
57.
ShaoHJiangHZhangH, et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech Syst Signal Process2018; 100: 743–765.
58.
DashBMBouamamaBOBoukerdjaM, et al. Bond graph-CNN based hybrid fault diagnosis with minimum labeled data. Eng Appl Artif Intell2024; 131: 107734.
59.
SchubertUKrugerUArellano-GarciaH, et al. Unified model-based fault diagnosis for three industrial application studies. Control Eng Pract2011; 19(5): 479–490.
60.
PCA, erdogant/pca: pca: A Python Package for Principal Component Analysis
61.
SunHLanQSiQ, et al. Cavitation detection via motor current signal analysis for a centrifugal pump in the pumped storage pump station. J Energy Storage2024; 81: 110417.
62.
CaoRYuanJZhaoF, et al. Probability-driven identification mechanism for degradation of magnetic drive pumps. Meas Sci Technol2022; 11(33): 115302.
63.
ZhangXZhaoSWangK.Influence of structural parameters of water-lubricated bearings on vibration characteristics of marine magnetic pumps. Water2023; 23(15): 4060.
64.
ZhangRWangJQianW, et al. Optimization of magnetic pump impeller based on blade load curve and internal flow study. Mathematics2024; 4(12): 607.
65.
XuZKongFZhangK, et al. Optimization of the impeller for hydraulic performance improvement of a high-speed magnetic drive pump. Adv Mech Eng2022; 6(14): 1–12.
66.
XuZKongFZhangK, et al. Internal flow field and loss analysis of a magnetic drive pump’s cooling circuit. Energies2023; 16(2): 840.
67.
KNN, ArthurBrussee/KNN: Fast K-Nearest Neighbour Library for Unity DOTS
68.
HintonGESalakhutdinovRR.Reducing the dimensionality of data with neural networks. Processes2006; 313(5785): 504–507.
XiaoDHuangYQinC, et al. Fault diagnosis of induction motors using recurrence quantification analysis and LSTM with weighted BN. Shock Vib2019; 2019: 1–14.
LiZGaoYZhangX, et al. A model-data-hybrid-driven diagnosis method for open-switch faults in power converters. IEEE Trans Power Electron2021; 36(5): 4965–4970.
73.
AtouiMAVerronSKobiA.A Bayesian network dealing with measurements and residuals for system monitoring. Trans Inst Meas Control2015; 38(4): 373–384.
74.
GhoshaKNgaYSSrinivasanR.Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods. Comput Chem Eng2011; 35(2): 342–355.
75.
LeeJSuH.A unified industrial large knowledge model framework in Industry 4.0 and smart manufacturing. Comput Sci Mach Learn2023; 2312: 14428.
76.
ParkDAnGKamyodC, et al. A study on performance improvement of prompt engineering for generative AI with a large language model. J Web Eng2024; 22(8): 1187–1206.
