Research Status and Challenges on Fault Diagnosis Methodology for Fuel Control System of Aero-engine

doi:10.3901/JME.2024.04.003

Abstract

Abstract: As engine performance requirements continue to improve, the operating conditions of the fuel control system have become harsher and the boundary conditions have become more complex. There are various causes of fatal failures in fuel control systems,including inherent pressure pulsation of the fuel pump and fluid-solid coupling vibration of pipelines and valves, leakage caused by corrosion or aging of sealing rings, increased wear due to oil contamination or lubricating oil failure, etc. At the same time, the fuel control system has the characteristics of few measuring points, variable operating conditions, strong interference, and strong nonlinearity. Therefore, there is an urgent need for fault diagnosis technology in this field while facing huge challenges. In order to promote the development of fault diagnosis technology in the field of fuel control systems, this study reviews the main methods and classifications of fault diagnosis technology after summarizing the characteristics and common faults of the fuel control system.Furthermore, from the perspective of hydraulic component interchangeability, the current research status of key components of fuel control systems is summarized based on physical models, signal processing and artificial intelligence diagnostic methods. Finally, the challenges and opportunities existing in fuel control system fault diagnosis technology are pointed out.

Key words: fuel control system, fault diagnosis, physical model, signal processing, artificial intelligence

CLC Number:

TH17

YAN Ruqiang, XU Wengang, WANG Zhiying, ZHU Qixiang, ZHOU Zheng, ZHAO Zhibin, SUN Chuang, WANG Shibin, CHEN Xuefeng, ZHANG Junhui, XU Bing. Research Status and Challenges on Fault Diagnosis Methodology for Fuel Control System of Aero-engine[J]. Journal of Mechanical Engineering, 2024, 60(4): 3-31.

References

[1] 姚华,黄金泉.航空发动机燃油及控制系统设计[M].北京:科学出版社,2022. YAO Hua,HUANG Jinquan. Design of aviation engine fuel and control system[M]. Beijing:Science Press,2022.
[2] LI J,KING S,JENNIONS I. Intelligent fault diagnosis of an aircraft fuel system using machine learning-A literature review[J]. Machines,2023,11(4):481.
[3] Air Accidents Investigation Branch. Report on the accident to boeing 777-236ER,G-YMMM,at London Heathrow airport on 17 January 2008[EB/OL].[2022-10-01]. https://www.gov.uk/aaib-reports/1-2010-boeing-777-236er-g-ymmm-17-january-2008.
[4] INSLEY J,TURKOGLU C. A contemporary analysis of aircraft maintenance-related accidents and serious incidents[J]. Aerospace,2020,7(6):81.
[5] Aircraft Accident Investigation Group of Bangladesh. Boeing 777-300ER aircraft serious incident investigation in bangladesh[EB/OL].[2022-10-01]. http://www.caab.gov.bd/aaig/S2-AFP.pdf.
[6] Transport Safety Investigation Bureau. B777-300ER,Registration 9V-SWB Engine Fire[EB/OL].[2022-10-01]. https://www.skybrary.aero/bookshelf/final-report-engine-fire-b777-300er-9v-swb-singapore-27-june-2016.
[7] JOHNSON S B,GORMLEY T,KESSLER S,et al. System health management:With aerospace applications[M]. Chichester:John Wiley&Sons,2011.
[8] BARTLETT L M,HURDLE E E,KELLY E M. Integrated system fault diagnostics utilising digraph and fault tree-based approaches[J]. Reliability Engineering&System Safety,2009,94(6):1107-1115.
[9] ZAPOROWSKA A,LIU H,SKAF Z,et al. A clustering approach to detect faults with multi-component degradations in aircraft fuel systems[J]. IFAC-PapersOnLine,2020,53(3):113-118.
[10] KELLY E M,BARTLETT L M. Aircraft fuel rig system fault diagnostics based on the application of digraphs[J]. Proceedings of the Institution of Mechanical Engineers,Part O:Journal of Risk and Reliability,2007,221(4):275-284.
[11] HURDLE E E,BARTLETT L M,ANDREWS J D. Fault diagnostics of dynamic system operation using a fault tree based method[J]. Reliability Engineering&System Safety,2009,94(9):1371-1380.
[12] LONG H,WANG X. Aircraft fuel system diagnostic fault detection through expert system[C]//2008 7th World Congress on Intelligent Control and Automation. IEEE,2008:7104-7107.
[13] JIGAJINNI V S,UPENDRANATH V. ANFIS-based fault diagnosis tool for a typical small aircraft fuel system[C]//Proceeding of International Conference on Intelligent Communication,Control and Devices:ICICCD 2016. Springer Singapore,2017:391-405.
[14] ZHANG X,PISU P. An unscented kalman filter based on-line diagnostic approach for PEM fuel cell flooding[J]. International Journal of Prognostics and Health Management,2014,5(1):1-18.
[15] RÍOS H,DAVILA J,FRIDMAN L,et al. Fault detection and isolation for nonlinear systems via high order sliding mode multiple observer[J]. International Journal of Robust and Nonlinear Control,2015,25(16):2871-2893.
[16] RAI A,UPADHYAY S H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J]. Tribology International,2016,96:289-306.
[17] SAIMURUGAN M,NITHESH R. Intelligent fault diagnosis model for rotating machinery based on fusion of sound signals[J]. International Journal of Prognostics and Health Management,2016,7(2):1-10.
[18] MURALIDHARAN V,SUGUMARAN V. A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis[J]. Applied Soft Computing,2012,12(8):2023-2029.
[19] SHEN T,WAN F,CUI W,et al. Application of prognostic and health management technology on aircraft fuel system[C]//2010 Prognostics and System Health Management Conference. IEEE,2010:1-7.
[20] LAMOUREUX B,MASSÉ J R,MECHBAL N. An approach to the health monitoring of a pumping unit in an aircraft engine fuel system[C]//PHM Society European Conference,2012,1(1):1-7.
[21] WANG H,SONG B,WAN F. Research on fault pattern recognition for aircraft fuel system with its performance simulation[C]//2011 2nd International Conference on Artificial Intelligence,Management Science and Electronic Commerce (AIMSEC). IEEE,2011:4231-4235.
[22] ZHANG S,LI M,ZHENG H,et al. Aircraft fuel system fuzzy FMEA and FMECA analysis[C]//First International Conference on Information Sciences,Machinery,Materials and Energy. Atlantis Press,2015:378-382.
[23] GAO Z,MA C,SONG D,et al. Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis[J]. Neurocomputing,2017,238:13-23.
[24] ZAPOROWSKA A,LIU H,SKAF Z,et al. A clustering approach to detect faults with multi-component degradations in aircraft fuel systems[J]. IFAC-PapersOnLine,2020,53(3):113-118.
[25] JIGAJINNI V S,UPENDRANATH V. Health management of a typical small aircraft fuel system using an adaptive technique[C]//2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW). IEEE,2018:104-111.
[26] LIU H,ZHAO Y,ZAPOROWSKA A,et al. A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems[J]. Neural Computing and Applications,2020:1-17.
[27] SKAF Z,ABDULMOUTI H,ALBLOOSHI S,et al. Fault detection for aircraft fuel system with machine learning techniques[C]//2023 Advances in Science and Engineering Technology International Conferences (ASET). IEEE,2023:1-6.
[28] JIAO X,JING B,HUANG Y,et al. Research on fault diagnosis of airborne fuel pump based on EMD and probabilistic neural networks[J]. Microelectronics Reliability,2017,75:296-308.
[29] BEARD R V. Failure accommodation in linear system through selfreorganization[D]. Massachusetts:Massachusetts Institute of Technology,1971.
[30] GAO Z,CECATI C,DING S X. A survey of fault diagnosis and fault-tolerant techniques-Part I:Fault diagnosis with model-based and signal-based approaches[J]. IEEE Transactions on Industrial Electronics,2015,62(6):3757-3767.
[31] MALAGO M,MUCCHI E,DALPIAZ G. Fault detection in heavy duty wheels by advanced vibration processing techniques and lumped parameter modeling[J]. Mechanical Systems and Signal Processing,2016,70:141-160.
[32] XUE S,HOWARD I. Torsional vibration signal analysis as a diagnostic tool for planetary gear fault detection[J]. Mechanical Systems and Signal Processing,2018,100:706-728.
[33] LI F,QIN Y,GE L,et al. Influences of planetary gear parameters on the dynamic characteristics-A review[J]. Journal of Vibroengineering,2015,17(2):574-586
[34] TIAN Z,ZUO M J,WU S. Crack propagation assessment for spur gears using model-based analysis and simulation[J]. Journal of Intelligent Manufacturing,2012,23:239-253.
[35] LIANG X H,LIU Z L,PAN J,et al. Spur gear tooth pitting propagation assessment using model-based analysis[J]. Chinese Journal of Mechanical Engineering,2017,30:1369-1382.
[36] ZHAI S,WANG W,YE H. Fault diagnosis based on parameter estimation in closed-loop systems[J]. IET Control Theory&Applications,2015,9(7):1146-1153.
[37] SOBHANI-TEHRANI E,TALEBI H A,KHORASANI K. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators[J]. Neural Networks,2014,50:12-32.
[38] NAGY-KISS A M,SCHUTZ G,RAGOT J. Parameter estimation for uncertain systems based on fault diagnosis using Takagi-Sugeno model[J]. ISA Transactions,2015,56:65-74.
[39] JIA F,CAO F,GUO Y,et al. Active fault diagnosis for a class of closed-loop systems via parameter estimation[J]. Journal of the Franklin Institute,2022,359(8):3979-3999.
[40] DE SOUZA R P P,AGULHARI C M,GOEDTEL A,et al. Inter-turn short-circuit fault diagnosis using robust adaptive parameter estimation[J]. International Journal of Electrical Power&Energy Systems,2022,139:107999.
[41] FENG Y,WANG Y,MO Y,et al. Computation-efficient fault detection framework for partially known nonlinear distributed parameter systems[J]. IEEE Transactions on Neural Networks and Learning Systems,2023:1-13.
[42] CASSEZ F,TRIPAKIS S. Fault diagnosis with static and dynamic observers[J]. Fundamenta Informaticae,2008,88(4):497-540.
[43] CAPISANI L M,FERRARA A,DE LOZA A F,et al. Manipulator fault diagnosis via higher order sliding-mode observers[J]. IEEE Transactions on Industrial Electronics,2012,59(10):3979-3986.
[44] ZHIRABOK A N,SHUMSKY A E,ZUEV A V. Fault diagnosis in linear systems via sliding mode observers[J]. International Journal of Control,2021,94(2):327-335.
[45] RAIMONDO D M,MARSEGLIA G R,BRAATZ R D,et al. Closed-loop input design for guaranteed fault diagnosis using set-valued observers[J]. Automatica,2016,74:107-117.
[46] BLESA J,JIMÉNEZ P,ROTONDO D,et al. An interval NLPV parity equations approach for fault detection and isolation of a wind farm[J]. IEEE Transactions on Industrial Electronics,2014,62(6):3794-3805.
[47] BLESA J,JIMÉNEZ P,ROTONDO D,et al. Fault diagnosis of a wind farm using interval parity equations[J]. IFAC Proceedings Volumes,2014,47(3):4322-4327.
[48] BECKERLE P,SCHAEDE H,BUTZEK N,et al. Balancing filters:An approach to improve model-based fault diagnosis based on parity equations[J]. Mechanical Systems and Signal Processing,2012,29:137-147.
[49] SONG H,HAN P,ZHANG J,et al. Fault diagnosis method for closed-loop satellite attitude control systems based on a fuzzy parity equation[J]. International Journal of Distributed Sensor Networks,2018,14(10):1550147718805938.
[50] MATTOS W J,ARAÚJO R E. Fault diagnosis in DC-DC power converters based on parity equations[C]//2020 International Young Engineers Forum (YEF-ECE). IEEE,2020:13-18.
[51] 何正嘉,訾艳阳,张西宁.现代信号处理及工程应用[M].西安:西安交通大学出版社,2007 HE Zhengjia,ZI Yanyang,ZHANG Xining. Modern signal processing and engineering applications[M]. Xi'an:Xi'an Jiaotong University Press,2007.
[52] RAI V K,MOHANTY A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007,21(6):2607-2615.
[53] SHABBIR M N S K,LIANG X. A DFFT and coherence analysis-based fault diagnosis approach for induction motors fed by variable frequency drives[C]//2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE,2020:1-5.
[54] LEE J S,YOON T M,LEE K B. Bearing fault detection of IPMSMs using zoom FFT[J]. Journal of Electrical Engineering&Technology,2016,11(5):1235-1241.
[55] IBARRA-ZARATE D,TAMAYO-PAZOS O,VALLEJO-GUEVARA A. Bearing fault diagnosis in rotating machinery based on cepstrum pre-whitening of vibration and acoustic emission[J]. The International Journal of Advanced Manufacturing Technology,2019,104:4155-4168.
[56] 屈梁生,陈岳东.计算机辅助监测与诊断技术[M].西安:西安交通大学出版社,1989. QU Liangsheng,CHEN Yuedong. Computer aided monitoring and diagnosis technology[M]. Xi'an:Xi'an Jiaotong University Press,1989.
[57] TONG C,WANG S,SELESNICK I,et al. Ridge-aware weighted sparse time-frequency representation[J]. IEEE Transactions on Signal Processing,2020,69:136-149.
[58] LIU H,LI L,MA J. Rolling bearing fault diagnosis based on STFT-deep learning and sound signals[J]. Shock and Vibration,2016,2016:6127479.
[59] CHEN J,LI Z,PAN J,et al. Wavelet transform based on inner product in fault diagnosis of rotating machinery:A review[J]. Mechanical Systems and Signal Processing,2016,70:1-35.
[60] WANG J,DU G,ZHU Z,et al. Fault diagnosis of rotating machines based on the EMD manifold[J]. Mechanical Systems and Signal Processing,2020,135:106443.
[61] CLIMENTE ALARCON V,ANTONINO DAVIU J A,RIERA-GUASP M,et al. Induction motor diagnosis by advanced notch FIR filters and the Wigner-Ville distribution[J]. IEEE Transactions on Industrial Electronics,2013,61(8):4217-4227.
[62] LEI Y,YANG B,JIANG X,et al. Applications of machine learning to machine fault diagnosis:A review and roadmap[J]. Mechanical Systems and Signal Processing,2020,138:106587.
[63] DUAN L,XIE M,WANG J,et al. Deep learning enabled intelligent fault diagnosis:Overview and applications[J]. Journal of Intelligent&Fuzzy Systems,2018,35(5):5771-5784.
[64] NAN C,KHAN F,IQBAL M T. Real-time fault diagnosis using knowledge-based expert system[J]. Process Safety and Environmental Protection,2008,86(1):55-71.
[65] YANG Z,KONG C,WANG Y,et al. Fault diagnosis of mine asynchronous motor based on MEEMD energy entropy and ANN[J]. Computers&Electrical Engineering,2021,92:107070.
[66] YIN Z,HOU J. Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes[J]. Neurocomputing,2016,174:643-650.
[67] MOOSAVIAN A,AHMADI H,TABATABAEEFAR A,et al. Comparison of two classifiers; K-nearest neighbor and artificial neural network,for fault diagnosis on a main engine journal-bearing[J]. Shock and Vibration,2013,20(2):263-272.
[68] ABDALLAH I,NTERTIMANIS V,MYLONAS C,et al. Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data[J]. Safety and Reliability-Safe Societies in a Changing World,2018:3053-3061.
[69] WANG X,QIN Y,WANG Y,et al. ReLTanh:An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis[J]. Neurocomputing,2019,363:88-98.
[70] ZHU J,HU T,JIANG B,et al. Intelligent bearing fault diagnosis using PCA-DBN framework[J]. Neural Computing and Applications,2020,32:10773-10781.
[71] EREN L,INCE T,KIRANYAZ S. A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier[J]. Journal of Signal Processing Systems,2019,91:179-189.
[72] WEN L,LI X,GAO L. A transfer convolutional neural network for fault diagnosis based on ResNet-50[J]. Neural Computing and Applications,2020,32:6111-6124.
[73] LONG M,WANG J,CAO Y,et al. Deep learning of transferable representation for scalable domain adaptation[J]. IEEE Transactions on Knowledge and Data Engineering,2016,28(8):2027-2040.
[74] XIE Y,ZHANG T. A transfer learning strategy for rotation machinery fault diagnosis based on cycle-consistent generative adversarial networks[C]//2018 Chinese automation congress (CAC). IEEE,2018:1309-1313.
[75] SHEN F,CHEN C,YAN R,et al. Bearing fault diagnosis based on SVD feature extraction and transfer learning classification[C]//2015 Prognostics and System Health Management Conference (PHM). IEEE,2015:1-6.
[76] SHAO S,MCALEER S,YAN R,et al. Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE Transactions on Industrial Informatics,2018,15(4):2446-2455.
[77] KARNIADAKIS G E,KEVREKIDIS I G,LU L,et al. Physics-informed machine learning[J]. Nature Reviews Physics,2021,3(6):422-440.
[78] BECKERLE P,SCHAEDE H,BUTZEK N,et al. Balancing filters:An approach to improve model-based fault diagnosis based on parity equations[J]. Mechanical Systems and Signal Processing,2012,29:137-147.
[79] NAN G,CHEN E,YANG A. Dynamic characteristics of centrifugal pump induced by fluid and motor excitation forces[J]. Journal of Vibroengineering,2015,17(7):3909-3918.
[80] NAN G,ZHANG Y. Influence of parameters on vibrations for centrifugal pump based on the modal dynamics method[C]//2015 International conference on Applied Science and Engineering Innovation. Atlantis Press,2015:2113-2118.
[81] ABDEL FATAH A A,HASSAN M A,LOTFY M,et al. Health monitoring of centrifugal pumps using digital models[J]. Journal of Dynamic Systems,Measurement,and Control,2019,141(9):091014.
[82] LUO X,YANG J. Analysis and research on vibration characteristics of nuclear centrifugal pumps rotor rubbing fault[C]//Journal of Physics:Conference Series. IOP Publishing,2022,2403(1):012014.
[83] YU T,SHUAI Z,WANG X,et al. Mechanism of the rotor-stator interaction in a centrifugal pump with guided vanes based on dynamic mode decomposition[J]. Physics of Fluids,2022,34(8):087103.
[84] LIU X,LIU J,JIA L,et al. Performance analysis of a centrifugal pump based on noise[J]. Science and Technology for the Built Environment,2021,27(9):1256-1268.
[85] KUMAR S R,INIYAL U S,HARSHITHA V,et al. Anomaly detection in centrifugal pumps using model based approach[C]//2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE,2022,1:427-433.
[86] MUCCHI E,DALPIAZ G,RIVOLA A. Elastodynamic analysis of a gear pump. Part II:Meshing phenomena and simulation results[J]. Mechanical Systems and Signal Processing,2010,24(7):2180-2197.
[87] DEL C D,CASTILLA R,RAUSH G A,et al. Numerical analysis of external gear pumps including cavitation[J]. Journal of Fluids Engineering,2012,134(8):081105.
[88] MUCCHI E,RIVOLA A,DALPIAZ G. Modelling dynamic behaviour and noise generation in gear pumps:Procedure and validation[J]. Applied Acoustics,2014,77:99-111.
[89] PELLEGRI M,VACCA A. A CFD-radial motion coupled model for the evaluation of the features of journal bearings in external gear machines[C]//Fluid Power Systems Technology. American Society of Mechanical Engineers,2015,57236:V001T01A025.
[90] FROSINA E,SENATORE A,RIGOSI M. Study of a high-pressure external gear pump with a computational fluid dynamic modeling approach[J]. Energies,2017,10(8):1113.
[91] WOO S,OPPERWALL T,VACCA A,et al. Modeling noise sources and propagation in external gear pumps[J]. Energies,2017,10(7):1068.
[92] GUO R,LI Y,SHI Y,et al. Research on identification method of wear degradation of external gear pump based on flow field analysis[J]. Sensors,2020,20(14):4058.
[93] DHOTE N,KHOND M,SANKPAL R. Wear material determination and parameters optimization of an external gear pump by Taguchi technique[J]. Materials Today:Proceedings,2023,72:679-686.
[94] XU W,WANG Z,ZHOU Z,et al. An advanced pressure pulsation model for external gear pump[J]. Mechanical Systems and Signal Processing,2023,187:109943.
[95] EDGE K A,DARLING J. Cylinder pressure transients in oil hydraulic pumps with sliding plate valves[J]. Proceedings of the Institution of Mechanical Engineers,Part B:Management and Engineering Manufacture,1986,200(1):45-54.
[96] EDGE K A,DARLING J. The pumping dynamics of swash plate piston pumps[J]. Journal of Dynamic Systems,Measurement,and Control,1989,111(2):307-312.
[97] WIECZOREK U,IVANTYSYNOVA M. Computer aided optimization of bearing and sealing gaps in hydrostatic machines-The simulation tool caspar[J]. International Journal of Fluid Power,2002,3(1):7-20.
[98] DANES L,VACCA A. The influence of line and fluid parameters on pressure ripple in hydraulic transmissions[J]. SN Applied Sciences,2020,2(7):1281.
[99] MANRING N D. The discharge flow ripple of an axial-piston swash-plate type hydrostatic pump[J]. Journal of Dynamic Systems,Measurement,and Control,2000,122(2):263-268.
[100] LI Z,BURTON R,NIKIFORUK P. Experimental simulation of piston leakage in an axial piston pump[C]//ASME International Mechanical Engineering Congress and Exposition,2005:9-15.
[101] BERGADA J M,KUMAR S,DAVIES D L,et al. A complete analysis of axial piston pump leakage and output flow ripples[J]. Applied Mathematical Modelling,2012,36(4):1731-1751.
[102] 马吉恩.轴向柱塞泵流量脉动及配流盘优化设计研究[D].杭州:浙江大学,2009. MA Jien. Study on flow ripple and valve plate optimization of axial piston pump[D]. Hangzhou:Zhejiang University,2009.
[103] 翟江.海水淡化高压轴向柱塞泵的关键技术研究[D].杭州:浙江大学,2012. ZHAI Jiang. Investigation into the key problems of axial piston pump for seawater desalination[D]. Hangzhou:Zhejiang University,2012.
[104] LÜ F,YE S,ZHANG J,et al. Theoretical and simulation investigations on flow ripple reduction of axial piston pumps using nonuniform distribution of pistons[J]. Journal of Dynamic Systems,Measurement,and Control,2021,143(4):041008.
[105] SHI C,WANG S,WANG X,et al. Wear modelling of slipper/swashplate pair for highspeed piston pump under transient lubrication conditions[C]//2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA). Chengdu,China:IEEE,2021:2124-2129.
[106] XIA S,XIA Y,XIANG J. Modelling and fault detection for specific cavitation damage based on the discharge pressure of axial piston pumps[J]. Mathematics,Multidisciplinary Digital Publishing Institute,2022,10(14):2461.
[107] YE S,ZHANG J,XU B,et al. Theoretical investigation of the contributions of the excitation forces to the vibration of an axial piston pump[J]. Mechanical Systems and Signal Processing,2019,129:201-217.
[108] YE S,ZHANG J,XU B,et al. A theoretical dynamic model to study the vibration response characteristics of an axial piston pump[J]. Mechanical Systems and Signal Processing,2021,150:107237.
[109] XU H,ZHANG J,SUN G,et al. The direct measurement of the cylinder block dynamic characteristics based on a non-contact method in an axial piston pump[J]. Measurement,2021,167:108279.
[110] ZHANG J,XU H,CHEN J,et al. Modeling and analysis of the tilt behavior of the cylinder block in a high-speed axial piston pump[J]. Mechanism and Machine Theory,2022,170:104735.
[111] ZHANG J,SHEN Y,GAN M,et al. Multi-objective optimization of surface texture for the slipper/swash plate interface in EHA pumps[J]. Frontiers of Mechanical Engineering,2022,17(4):48.
[112] SHANG L,IVANTYSYNOVA M. Scaling criteria for axial piston machines based on thermo-elastohydrodynamic effects in the tribological interfaces[J]. Energies,2018,11(11):3210.
[113] CHACON R,IVANTYSYNOVA M. Virtual prototyping of axial piston machines:Numerical method and experimental validation[J]. Energies,2019,12(9):1674.
[114] 王克龙.轴向柱塞泵柱塞副微运动及润滑油膜的特性研究[D].哈尔滨:哈尔滨工业大学,2019. WANG Kelong. Micro-motion and lubrication characteristics of piston-cylinder interface in axial piston pump[D]. Harbin:Harbin Institute of Technology,2019.
[115] XU B,SHEN J,LIU S,et al. Research and development of electro-hydraulic control valves oriented to industry 4.0:A review[J]. Chinese Journal of Mechanical Engineering,2020,33(1):1-20.
[116] ZHANG J,WANG D,XU B,et al. Flow control of a proportional directional valve without the flow meter[J]. Flow Measurement and Instrumentation,2019,67:131-141.
[117] YANG M,ZHANG J,XU B. Experimental study and simulation analysis on electromagnetic characteristics and dynamic response of a new miniature digital valve[J]. Advances in Materials Science and Engineering,2018,2018:1-8.
[118] LU Z,ZHANG J,XU B,et al. Deadzone compensation control based on detection of micro flow rate in pilot stage of proportional directional valve[J]. ISA Transactions,2019,94:234-245.
[119] ZHANG J,LU Z,XU B,et al. Investigation on the dynamic characteristics and control accuracy of a novel proportional directional valve with independently controlled pilot stage[J]. ISA Transactions,2019,93:218-230.
[120] LI J,XIAO M,LIANG Y,et al. Three-dimensional simulation and prediction of solenoid valve failure mechanism based on finite element model[C]//IOP Conference Series:Earth and Environmental Science. IOP Publishing,2018,108(2):022035.
[121] LI J,XIAO M,SUN Y,et al. Failure mechanism study of direct action solenoid valve based on thermal-structure finite element model[J]. IEEE Access,2020,8:58357-58368.
[122] ANGADI S V,JACKSON R L,CHOE S Y,et al. Reliability and life study of hydraulic solenoid valve. Part 1:A multi-physics finite element model[J]. Engineering Failure Analysis,2009,16(3):874-887.
[123] ANGADI S V,JACKSON R L,CHOE S,et al. Reliability and life study of hydraulic solenoid valve. Part 2:Experimental study[J]. Engineering Failure Analysis,2009,16(3):944-963.
[124] JO S H,SEO B,OH H,et al. Model-based fault detection method for coil burnout in solenoid valves subjected to dynamic thermal loading[J]. IEEE Access,2020,8:70387-70400.
[125] PEDERSEN H C,BAK-JENSEN T,JESSEN R H,et al. Temperature-independent fault detection of solenoid-actuated proportional valve[J]. IEEE/ASME Transactions on Mechatronics,2022,27(6):4497-4506.
[126] RADUENZ H,MENDOZA Y E A,FERRONATTO D,et al. Online fault detection system for proportional hydraulic valves[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering,2018,40:1-20.
[127] YANG L,LIU J,SUN R,et al. Spline adaptive filters based on real-time over-sampling strategy for nonlinear system identification[J]. Nonlinear Dynamics,2021,103:657-675.
[128] YLINEN A,MARJAMÄKI H,MÄKINEN J. A hydraulic cylinder model for multibody simulations[J]. Computers&Structures,2014,138:62-72.
[129] QI H,ZHAO D,LIU D,et al. Double redundancy electro-hydrostatic actuator fault diagnosis method based on progressive fault diagnosis method[J]. Actuators,Multidisciplinary Digital Publishing Institute,2022,11(9):264.
[130] JAISWAL S,SOPANEN J,MIKKOLA A. Efficiency comparison of various friction models of a hydraulic cylinder in the framework of multibody system dynamics[J]. Nonlinear Dynamics,2021,104(4):3497-3515.
[131] ORTIZ J F,BORRÁS C,SEPULVEDA D. Fault detection and diagnosis for a nonlinear hydraulic actuator[J]. IOP Conference Series:Materials Science and Engineering,IOP Publishing,2019,575(1):012014.
[132] GARIMELLA P,YAO B. Fault detection of an electro-hydraulic cylinder using adaptive robust observers[C]//Fluid Power Systems and Technology. Anaheim,California,USA:ASMEDC,2004:119-128.
[133] LIU Y,TIAN L,LI Z,et al. Fault modeling of electro hydraulic actuator in gas turbine control system based on Matlab/Simulink[C]//2021 33rd Chinese Control and Decision Conference (CCDC). Kunming,China:IEEE,2021:2086-2091.
[134] RUDERMAN M. Full-and reduced-order model of hydraulic cylinder for motion control[C]//IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society. 2017:7275-7280.
[135] GAMEZ-MONTERO P J,SALAZAR E,CASTILLA R,et al. Misalignment effects on the load capacity of a hydraulic cylinder[J]. International Journal of Mechanical Sciences,2009,51(2):105-113.
[136] MADDAHI A,SEPEHRI N,KINSNER W. A practical approach for designing fault-tolerant position controllers in hydraulic actuators:Methodology and experimental validation[J]. Journal of Dynamic Systems,Measurement,and Control,2020,142(8):081001.
[137] SAEEDZADEH A,HABIBI S,ALAVI M,et al. A robust model-based strategy for real-time fault detection and diagnosis in an electro-hydraulic actuator using updated interactive multiple model smooth variable structure filter[J]. Journal of Dynamic Systems,Measurement,and Control,2023,145(10):101006.
[138] IJAZ S,YAN L,HAMAYUN M T,et al. Active fault tolerant control scheme for aircraft with dissimilar redundant actuation system subject to hydraulic failure[J]. Journal of the Franklin Institute,2019,356(3):1302-1332.
[139] DAO H V,TRAN D T,AHN K K. Active fault tolerant control system design for hydraulic manipulator with internal leakage faults based on disturbance observer and online adaptive identification[J]. IEEE Access,2021,9:23850-23862.
[140] PHAN V D,TRUONG H V A,AHN K K. Actuator failure compensation-based command filtered control of electro-hydraulic system with position constraint[J]. ISA Transactions,2023,134:561-572.
[141] DU PHAN V,AHN K K. Disturbance observer-based adaptive fault-tolerant control of an electro-hydraulic actuator with output constraint[C]//2022 13th Asian Control Conference (ASCC). 2022:1156-1160.
[142] SONG C,YANG Y. Nonlinear-observer-based neural fault-tolerant control for a rehabilitation exoskeleton joint with electro-hydraulic actuator and error constraint[J]. Applied Sciences,Multidisciplinary Digital Publishing Institute,2023,13(14):8294.
[143] CHAI Q,ZENG J,MA H,et al. A dynamic modeling approach for nonlinear vibration analysis of the L-type pipeline system with clamps[J]. Chinese Journal of Aeronautics,2020,33(12):3253-3265.
[144] GAO P,ZHAI J,YAN Y,et al. A model reduction approach for the vibration analysis of hydraulic pipeline system in aircraft[J]. Aerospace Science and Technology,2016,49:144-153.
[145] MIKOTA G,HAAS R,LUKACHEV E. Compensation of a hydraulic pipeline resonance by fluid-structure interaction[J]. International Journal of Fluid Power,Taylor&Francis,2018,19(1):14-26.
[146] GAO P,ZHAI J,HAN Q. Dynamic response analysis of aero hydraulic pipeline system under pump fluid pressure fluctuation[J]. Proceedings of the Institution of Mechanical Engineers,Part G:Journal of Aerospace Engineering,IMECHE,2019,233(5):1585-1595.
[147] DOU J,YANG T,YU X,et al. Model-driven fault diagnosis of slant cracks in aero-hydraulic straight pipes[J]. Advances in Mechanical Engineering,SAGE Publications,2020,12(9):1687814020954970.
[148] SAMANIPOUR P,POSHTAN J,SADEGHI H. Cavitation detection in centrifugal pumps using pressure time-domain features[J]. Turkish Journal of Electrical Engineering and Computer Sciences,2017,25(5):4287-4298.
[149] AL-OBAIDI A R,TOWSYFYAN H. An experimental study on vibration signatures for detecting incipient cavitation in centrifugal pumps based on envelope spectrum analysis[J]. Journal of Applied Fluid Mechanics,2019,12(6):2057-2067.
[150] LIANG X,LUO Y,DENG F,et al. Investigation on vibration signal characteristics in a centrifugal pump using EMD-LS-MFDFA[J]. Processes,2022,10(6):1169.