智能化雷达故障预测及检测技术综述

doi:10.11896/jsjkx.220400096

摘要/Abstract

摘要： 雷达故障预测和故障检测技术是雷达装备维护从传统化定期检修向智能化视情维修转变的关键技术。为保障雷达作战效能的发挥,需及时对雷达故障进行预测、检测并实时告警。随着微波测量和人工智能技术的日趋成熟,智能化雷达故障预测及检测技术也不断发展。文中详细阐述了当前故障预测与健康管理以及故障检测技术的国内外研究现状,分析了现有智能化雷达故障预测和检测技术的优缺点,梳理了该技术在雷达维修保障领域的研究进展,提出了雷达故障预测和检测过程中可能存在的问题和限制条件。针对实际问题和限制条件,对未来智能化雷达故障预测和检测技术的研究方向进行了展望,为智能化故障预测及故障检测技术在雷达维修保障领域的深入研究提供参考。

关键词: 预测与健康管理, 故障预测, 故障检测, 雷达故障

Abstract: Radar fault prediction and fault detection technology is the key technology for the transformation of radar equipment maintenance from traditional regular maintenance to intelligent condition-based maintenance.To ensure the performance of radar combat effectiveness,it is necessary to predict,detect and give real-time warnings to radar fault in time.With the maturity of microwave measurement and artificial intelligence technology,intelligent radar fault prediction and detection technology also conti-nues to develop.In this paper,the current research status of fault prediction and health management and fault detection technology at home and abroad are elaborated,the advantages and disadvantages of the existing intelligent radar fault prediction and detection technology are analyzed,the research progress of this technology in the field of radar maintenance support has been sorted out,possible problems and limitations in radar failure prediction and detection are presented.Aiming at the actual problems and constraints,the future research direction of intelligent radar fault prediction and detection technology are prospected,which can provide a reference for the in-depth research of intelligent fault prediction and fault detection technology in the field of radar maintenance support.

Key words: Prognostic and health management, Fault prediction, Fault detection, Radar fault

中图分类号:

TN956

翟玉婷, 程占昕, 房少军. 智能化雷达故障预测及检测技术综述[J]. 计算机科学, 2023, 50(5): 217-229. https://doi.org/10.11896/jsjkx.220400096

ZHAI Yuting, CHENG Zhanxin, FANG Shaojun. Overview of Intelligent Radar Fault Prediction and Detection Technology[J]. Computer Science, 2023, 50(5): 217-229. https://doi.org/10.11896/jsjkx.220400096

参考文献

[1]DING L F,GENG F L,CHEN J C.Radar Principles[M].Beijing:Publishing House of Electronics Industry,2016:1-20.
[2]AHMADI A,FRANSSON T,CRONA A,et al.Integration ofRCM and PHM for the next generation of air-craft[C]//Proceedings of 2009 IEEE Aerospace Conference.Big Sky,MT,USA,2009:1-9.
[3]JIGAJINNI V S,UPENDRANATH V.Health management of a typical small aircraft fuel system using an adaptive technique[C]//The Proceedings of 2018 IEEE 25th International Conference on High Performance Computing Workshops.Bengaluru,India,2018 :104-111.
[4]ZHANG B,ZHENG K,LUO J,et al.Informative sensor selection and health indicator construction for aircraft engines prognosis[C]//The Proceedings of 2019 International Conference on Sensing,Diagnostics,Prognostics,and Control.Beijing,China,2019:442-442.
[5]ELLEFSEN A L,BJØRLYKHAUG E,AESØY V,et al.An unsupervised reconstruction-based fault detection algorithm for maritime components[J].IEEE Access,2019,7:16101-16109.
[6]WANG Y,BAO C,DING F.Fault diagnosis of measurementsystem for dynamic positioning ship based on NN-SVM method[C]//The Proceedings of 2018 IEEE International Conference on Mechatronics and Automation.Changchun,China,2018:1361-1365.
[7]GANG Z,MINGHUI L.Research on classified prognostic and health management scheme for key components of nuclear power plant[C]//The Proceedings of 14th IEEE International Conference on Electronic Measurement & Instruments.Changsha,China,2019:1185-1191.
[8]AIZPURUA J I,MCARTHUR S D J,STEWART B G,et al.Adaptive power transformer life time predictions through machine learning and uncertainty modeling in nuclear power plants[J].IEEE Transactions on Industrial Electronics,2019,66(6):4726-4737.
[9]JIN X,SUN Y,QUE Z,et al.Anomaly detection and fault prognosis for bearings[J].IEEE Transactions on Instrumentation and Measurement,2016,65(9):2046-2054.
[10]LEE J,WU F,ZHAO W,et al.Prognostics and health management design for rotary machinery systems reviews,methodology and applications[J].Mechanical Systems and Signal Processing,2014,42(1/2):314-334.
[11]VOGL G W,WEISS B A,HELU M.A review of diagnostic and prognostic capabilities and best practices for manufacturing[J].Journal of Intelligent Manufacturing,2019,30(1):79-95.
[12]HESS A,FILA L.The joint strike fighter (JSF) PHM concept:potential impact on aging aircraft problems[C]//The Procee-dings of IEEE Aerospace Conference.Big Sky,MT,USA,2002:21-26.
[13]BAROTH E,POWERS W,FOX J,et al.IVHM (Integrated Vehicle Health Management) techniques for future space vehicles[C]//The Proceedings of 37th Joint Propulsion Conference and Exhibit.Salt Lake City,Utah,2001:1-10.
[14]BOEING.Boeing selects airlines to test airplane health management service[EB/OL].[2022-02-24].https://boeing.media-room.com/2003-04-16-Boeing-Selects-Airlines-to-Test-Airplane-Health-Management-Service.
[15]GENERAL MOTORS.Why OnStar[EB/OL].[2022-02-24].https://www.onstar.com/us/en/why-onstar.
[16]PENG Y,LIU D,PENG X.A review:Prognostics and healthmanagement[J].Journal of Electronic Measurement and Instrument,2010,24(1):1-9.
[17]KOGA M,MATSUMURA T,KAWABE R.Super advancedship operation support system[C]//The Proceedings of the Fourth International Offshore and Polar Engineering Confe-rence.Osaka,Japan,1994:1-8.
[18]AZZAM H,KNIGHT P,ELLISON R,et al.Fleet and usage management system (FUMSTM) technologies for gas turbines[C]//The Proceedings of the Turbo Expo 2006:Power for Land,Sea,and Air.Barcelona,Spain,2006:735-743.
[19]TRAMMEL C,VOSSLER G,FELDMANN M.UK Ministry of defence generic health and usage monitoring system(Gen-HUMS)[J].Aircraft Engineering and Aerospace Technology,1997,69(5):414-422.
[20]ZENG X,CHEN M.A novel big data collection system for ship energy efficiency monitoring and analysis based on BeiDou system[J].Journal of Advanced Transportation,2021,4(8):9914720.
[21]The State Council of the People's Republic of China.Outline of the National Medium and Long-Term Science and Technology Development Plan,[2006] No.9[EB/OL].[2022-02-24].http://www.gov.cn/gongbao/content/2006/content_240244.htm.
[22]PENG X Y,PENG Y,LIU D T.Data Driven Prognostics andHealth Management[M].Harbin:Harbin Institute of Technology Press,2006:275-276.
[23]GAO Z B,LIANG X,LI X S.Complex system integrated health management[J].Measurement & Control Technology,2005(8):1-5.
[24]ZENG S K,MICHAEL G P,WU J.Status and perspectives of prognostics and health management technologies[J].Acta Aeronautica et Astronautica Sinica,2005(5):626-632.
[25]MU Z G,HU H F,HU N Q.Design of prognostic and health management system for weapon equipment[J].Ordnance Industry Automation,2006(3):20-21.
[26]LI A J,ZHANG W G,TAN J.Survey on aircraft health management technology[J].Electronics Optics & Control,2007(3):79-83.
[27]SUN B,KANG R,XIE J S.The application technique of prognostic and health management system[J].Measurement & Control Technology,2007(7):12-14.
[28]ZHANG J Z,ZHANG L G.Aviation equipment failure prediction and health management equipment[J].Aeronautical Manufacturing Technology,2008(2):40-43.
[29]ZHANG L,ZHANG F M,LI J T,et al.Research on onboard prognostics and health management system architecture for operational aircraft[J].Journal of Air Force Engineering University (Natural Science Edition),2008(2):6-9.
[30]WANG X W.Aeroengine maintenance decision support system development based on performance parameters prediction[D].Harbin:Harbin Institute of Technology,2013.
[31]ZHOU J.Research on data-driven prediction methods for remaining useful life of aero-engine[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2017.
[32]XIE N,LEI J N.A technical research of prognostics and health management architecture on civil aircraft[J].Computer Mea-surement & Control,2020,28(2):19-22.
[33]YANG L F,WANG L,FENG J C.Maintenance support of missile based on PHM technology[J].Journal of Naval Aeronautical and Astronautical University,2010,25(4):447-450.
[34]GAO M J,LIU B L,ZHANG Z H.Research on prognostic and health management of fire control system[J].Fire Control & Command Control,2015,40(5):1-4.
[35]ZHANG Y,JIANG H C.Research on health management system of large-caliber artillery based on deep learning[J].Compu-ter Engineering & Science,2020,42(11):2050-2058.
[36]YANG S,DI C A,LI D H,et al.A fast signal processing method for PHM of artillery fire system[J].Automation & Instrumentation,2021,36(12):67-71.
[37]MA J,LV C,TAO L F,et al.Design of prognostics and health management for marine main propulsion system[J].Journal of Nanjing University of Aeronautics & Astronautics,2011,43(S1):119-124.
[38]YAO X S,ZHANG W D,ZHOU P,et al.Research on marine diesel's fault prognostic and health management based on oil monitoring[J].Journal of Wuhan University of Technology (Transportation Science & Engineering),2014,38(4):874-877.
[39]HAN X.Automatic life test of marine electrical equipment based on machine learning[J].Ship Science and Technology,2021,43(8):193-195.
[40]HUANG Z W,WEI X Y,LI S B.Architecture design of fault prognostics and health management for track circuit[J].Computer Engineering,2012,38(20):232-235.
[41]WANG Q,HE Z Y,LIN S,et al.PHM and active maintenance for high-speed railway traction power supply system[J].Journal of Southwest Jiaotong University,2015,50(5):942-952.
[42]CHEN Z,ZHANG S,WANG L,et al.Research on the overall scheme of electric locomotive fault prognostics and health mana-gement system[J].Electric Drive for Locomotives,2021(3):125-131.
[43]SUN X S,ZHOU G,YU Y,et al.Overview of prognostics and health management of mechanical equipment[J].Ordnance Industry Automation,2016,35(1):30-33.
[44]CHEN Z Q,CHEN X D,JOSÉ V D O,et al.Application of deep learning in equipment prognostics and health management[J].Chinese Journal of Scientific Instrument,2019,40(9):206-226.
[45]ZHOU J B,WANG S J,MA L P,et al.Study on the reconfigurable remaining useful life estimation system for satellite li-thium-ion battery[J].Chinese Journal of Scientific Instrument,2013,34(9):2034-2044.
[46]LI G,YU C H,LIU Y P,et al.Challenges and prospects of fault prognostic and health management for power transformer[J].Automation of Electric Power Systems,2017,41(23):156-167.
[47]HU J,HU X S,DENG Z W,et al.Data-driven comprehensiveevaluation of lithium-ion battery state of health and abnormal battery screening[J].Journal of Mechanical Engineering,2021,57(14):141-149.
[48]GAVER D P.Random hazard in reliability problems[J].Tech-nometrics,1963,5(2):211-226.
[49]PRENTICE R L,KALBFLEISCH J D,PETERSON J A V,et al.The analysis of failure times in the presence of competing risks[J].Biometrics,1978,34(4):541-554.
[50]MUDHOLKAR G S,SRIVASTAVA D K.ExponentiatedWeibull family for analyzing bathtub failure-rate data[J].IEEE Transactions on Reliability,1993,42(2):299-302.
[51]ENGLISH J R,YAN L,LANDERS T L.A modified bathtub curve with latent failures[C]//The Proceedings of the Annual Reliability and Maintainability Symposium 1995 Proceedings.Washington,DC,USA,1995:217-222.
[52]TANG Y,XIE M,GOH T N.Statistical analysis of a Weibull extension model[J].Communications in Statistics-Theory and Methods,2003,32(5):913-928.
[53]SUHIR E.Statistics-related and reliability-physics-related fai-lure processes in electronics devices and products[J].Modern Physics Letters B,2014,28(13):1450105.
[54]KUMAMOTO H,TANAKA K,INOUE K.Efficient evaluation of system reliability by Monte Carlo method[J].IEEE Transactions on Reliability,1977,26(5):311-315.
[55]KUMAMOTO H,TANAKA T,INOUE K.A new Monte Carlo method for evaluating system-failure probability[J].IEEE Transactions on Reliability,1987,36(1):63-69.
[56]NAESS A,LEIRA B J,BATSEVYCH O.System reliabilityanalysis by enhanced Monte Carlo simulation[J].Structural safety,2009,31(5):349-355.
[57]TCHÓRZEWSKA-CIEŚLAK B,PIETRUCHA-URBANIK K,URBANIK M.Analysis of the gas network failure and failure prediction using the Monte Carlo simulation method[J].Eksploatacja i Niezawodność,2016,18(2):254-259.
[58]SICHANI M T,NIELSEN S R,NAESS A.Failure probability estimation of wind turbines by enhanced Monte Carlo method[J].Journal of Engineering Mechanics,2012,138(4):379-389.
[59]RODRÍGUEZ J A,GARCIA J C,ALONSO E,et al.Failureprobability estimation of steam turbine blades by enhanced Monte Carlo method[J].Engineering Failure Analysis,2015,56:80-88.
[60]GUÉRIN F,BARREAU M,CHARKI A,et al.Bayesian estimation of failure probability in mechanical systems using Monte Carlo simulation[J].Quality Technology & Quantitative Mana-gement,2007,4(1):51-70.
[61]TAHERIYOUN M,MORADINEJAD S.Reliability analysis of a wastewater treatment plant using fault tree analysis and Monte Carlo simulation[J].Environmental Monitoring and Assessment,2015,187(1):1-13.
[62]RAY A,TANGIRALA S.Stochastic modeling of fatigue crackdynamics for on-line failure prognostics[J].IEEE Transactions on Control Systems Technology,1996,4(4):443-451.
[63]LESIEUTRE G A,FANG L,LEE U.Hierarchical failure simulation for machinery prognostics[C]//Critical Link:Diagnosis to Prognosis,Haymarket.1997:103-110.
[64]LI Y,BILLINGTON S,ZHANG C,et al.Adaptive prognostics for rolling element bearing condition[J].Mechanical Systems and Signal Processing,1999,13(1):103-113.
[65]LI Y,KURFESS T R,LIANG S Y.Stochastic prognostics for rolling element bearings[J].Mechanical Systems and Signal Processing,2000,14(5):747-762.
[66]ADAMS D E.Nonlinear damage models for diagnosis and prognosis in structural dynamic systems[C]//The Proceedings of the Component and Systems Diagnostics,Prognostics,and Health Management II.Orlando,FL,USA,2002:180-191.
[67]CHELIDZE D.Multimode damage tracking and failure prognosis in electromechanical systems[C]//The Proceedings of the Component and Systems Diagnostics,Prognostics,and Health Management II.Orlando,FL,USA,2002:1-12.
[68]LUO J,BIXBY A,PATTIPATI K,et al.An interacting multiple model approach to model-based prognostics[C]//The Procee-dings of 2003 IEEE International Conference on Systems,Man and Cybernetics.Washington,DC,USA,2003:189-194.
[69]JIA Q,ZHANG Y.Quality-related fault detection approachbased on dynamic kernel partial least squares[J].Chemical Engineering Research and Design,2016,106:242-252.
[70]JIAO J,YU H,WANG G.A quality-related fault detection approach based on dynamic least squares for process monitoring[J].IEEE Transactions on Industrial Electronics,2015,63(4):2625-2632.
[71]QIAN Y,YAN R.Remaining useful life prediction of rollingbearings using an enhanced particle filter[J].IEEE Transactions on Instrumentation and Measurement,2015,64(10):2696-2707.
[72]LI B A,LIU Z H,LI X J.Research of UAV engine fault prediction based on particle filter[C]//The Proceedings of 2009 9th International Conference on Electronic Measurement & Instruments.Beijing,China,2009:813-817.
[73]BRAHIM-BELHOUARI S,BERMAK A.Gaussian process fornonstationary time series prediction[J].Computational Statistics &Data Analysis,2004,47(4):705-712.
[74]SUN W,CHEN J,LI J.Decision tree and PCA based fault diagnosis of rotating machinery[J].Mechanical Systems and Signal Processing,2007,21(3):1300-1317.
[75]JIARULA Y,GAO J,GAO Z,et al.Fault mode prediction based on decision tree[C]//The Proceedings of 2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference.Xi'an,China,2016:1729-1733.
[76]WONG K C P,RYAN H M,TINDLE J.Power system faultprediction using artificial neural networks[C]//The Proceedings of Progress in Neural Information Processing.London,UK:Springer,1996:1181-1186.
[77]KHOSHGOFTAAR T M,SZABO R M.Using neural networks to predict software faults during testing[J].IEEE Transactions on Reliability,1996,45(3):456-462.
[78]JAUDET M,IQBAL N,HUSSAIN A.Neural networks forfault-prediction in a telecommunications network[C]//The Proceedings of the 8th International Multitopic Conference.Lahore,Pakistan,2004:315-320.
[79]YAM RC M,TSE P W,LI L,et al.Intelligent predictive decision support system for condition based maintenance[J].The International Journal of Advanced Manufacturing Technology,2001,17(5):383-391.
[80]WANG P,VACHTSEVANOS G.Fault prognostics using dy-namic wavelet neural networks[J].Artificial Intelligence for Engineering Design,Analysis and Manufacturing,2001,15(4):349-365.
[81]WANG W Q,GOLNARAGHI M F,ISMAIL F.Prognosis ofmachine health condition using neuro-fuzzy systems[J].Mechanical Systems and Signal Processing,2004,18(4):813-831.
[82]YIN Z,HOU J.Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes[J].Neurocomputing,2016,174:643-650.
[83]HOU S,LI Y.Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy[J].Expert Systems with Applications,2009,36(10):12383-12391.
[84]KUMAR L,SRIPADA S K,SUREKA A,et al.Effective fault prediction model developed using least square support vector machine (LSSVM)[J].Journal of Systems and Software,2018,137:686-712.
[85]GOMEZ F R,RAJAPAKSE A D,ANNAKKAGE U D,et al.Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements[J].IEEE Transactions on Power Systems,2010,26(3):1474-1483.
[86]WU Y,YUAN M,DONG S,et al.Remaining useful life estimation of engineered systems using vanilla LSTM neural networks[J].Neurocomputing,2018,275:167-179.
[87]JANSSENS O,VAN DE W R,LOCCUFIER M,et al.Deeplearning for infrared thermal image based machine health monitoring[J].IEEE/ASME Transactions on Mechatronics,2017,23(1):151-159.
[88]CAO Y K,CHAO J Y,WANG X F.Prediction of wind turbine gear belt breakage based on the LSTM neural network[J].Electrical Automation,2019,41(4):92-95.
[89]LIAO L,JIN W,PAVEL R.Enhanced restricted Boltzmann machine with prognosability regularization for prognostics and health assessment[J].IEEE Transactions on Industrial Electronics,2016,63(11):7076-7083.
[90]CHEN J,JING H,CHANG Y,et al.Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process[J].Reliability Engineering & System Safety,2019,185:372-382.
[91]DEUTSCH J,HE D.Using deep learning-based approach topredict remaining useful life of rotating components[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2017,48(1):11-20.
[92]ZHANG W,WANG J,WANG J,et al.Short-term wind speed forecasting based on a hybrid model[J].Applied Soft Computing,2013,13(7):3225-3233.
[93]LIU H,TIAN H,LI Y.Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction[J].Applied Energy,2012,98:415-424.
[94]KHOSHGOFTAAR T M,SELIYA N.Tree-based softwarequality estimation models for fault prediction[C]//The Procee-dings of 8th IEEE Symposium on Software Metric.Ottawa,ON,Canada,2002:203-214.
[95]ILLIAS H A,CHAI X R,ABU BAKAR A H,et al.Transfor-mer incipient fault prediction using combined artificial neural network and various particle swarm optimisation techniques[J].Plos One,2015,10(6):0129363.
[96]ZHANG S,WANG Y,LIU M,et al.Data-based line trip fault prediction in power systems using LSTM networks and SVM[J].IEEE Access,2017,6:7675-7686.
[97]SUGUMARAN V,MURALIDHARAN V,RAMACHAND-RAN K I.Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing[J].Mechanical Systems and Signal Processing,2007,21(2):930-942.
[98]ZHAO R,WANG D,YAN R,et al.Machine health monitoring using local feature-based gated recurrent unit networks[J].IEEE Transactions on Industrial Electronics,2017,65(2):1539-1548.
[99]ZHANG Y,XIONG R,HE H,et al.Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries[J].IEEE Transactions on Vehicular Technology,2018,67(7):5695-5705.
[100]KOTU V,DESHPANDE B.Predictive analytics and data mi-ning:concepts and practice with rapidminer[M].Waltham:Morgan Kaufmann,2014:329-346.
[101]ALLA S,ADARI S K.Beginning anomaly detection using Python-based deep learning[M].New Jersey:Apress,2019:1-23.
[102]GÖRNITZ N,KLOFT M,RIECK K,et al.Toward supervised anomaly detection[J].Journal of Artificial Intelligence Research,2013,46:235-262.
[103]ABBASION S,RAFSANJANI A,FARSHIDIANFAR A,et al.Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine[J].Mechanical Systems and Signal Processing,2007,21(7):2933-2945.
[104]JING C,HOU J.SVM and PCA based fault classification ap-proaches for complicated industrial process[J].Neurocompu-ting,2015,167:636-642.
[105]JAN S U,LEE Y D,SHIN J,et al.Sensor fault classificationbased on support vector machine and statistical time-domain features[J].IEEE Access,2017,5:8682-8690.
[106]DALSTEIN T,KULICKE B.Neural network approach to fault classification for high speed protective relaying[J].IEEE Transa-ctions on Power Delivery,1995,10(2):1002-1011.
[107]KNAPP G M,WANG H P.Machine fault classification:a neural network approach[J].International Journal of Production Research,1992,30(4):811-823.
[108]WANG H,KEERTHIPALA W W L.Fuzzy-neuro approach to fault classification for transmission line protection[J].IEEE Transactions on Power Delivery,1998,13(4):1093-1104.
[109]HONGCHUN S,XIANGFEI S,DAJUN S.A new method forlocating faults on transmission lines based on rough set and FNN[C]//The Proceedings of the International Conference on Power System Technology.Kunming,China,2002:2584-2588.
[110]LEE K B,CHEON S,KIM C O.A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes[J].IEEE Transactions on Semi-conductor Manufacturing,2017,30(2):135-142.
[111]JIA F,LEI Y,LU N,et al.Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization[J].Mechanical Systems and Signal Processing,2018,110:349-367.
[112]SAMANTARAY S R.Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line[J].IET generation,transmission & distribution,2009,3(5):425-436.
[113]JAMEHBOZORG A,SHAHRTASH S M.A decision-tree-based method for fault classification in single-circuit transmission lines[J].IEEE Transactions on Power Delivery,2010,25(4):2190-2196.
[114]JIANG L,GE Z,SONG Z.Semi-supervised fault classification based on dynamic sparse stacked auto-encoders model[J].Chemometrics and Intelligent Laboratory Systems,2017,168:72-83.
[115]YIN L,WANG H,FAN W,et al.Incorporate active learning to semi-supervised industrial fault classification[J].Journal of Process Control,2019,78:88-97.
[116]ZHAO Y,BALL R,MOSESIAN J,et al.Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays[J].IEEE Transactions on Power Electro-nics,2014,30(5):2848-2858.
[117]SELIYA N,KHOSHGOFTAAR T M.Software quality estimation with limited fault data:a semi-supervised learning perspective[J].Software Quality Journal,2007,15(3):327-344.
[118]YAN K,ZHONG C,JI Z,et al.Semi-supervised learning for early detection and diagnosis of various air handling unit faults[J].Energy and Buildings,2018,181:75-83.
[119]ANGIULLI F,PIZZUTI C.Fast outlier detection in high dimensional spaces[C]//The Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery.Berlin:Springer,2002:15-27.
[120]HAUTAMAKI V,KARKKAINEN I,FRANTI P.Outlier de-tection using k-nearest neighbour graph[C]//The Proceedings of 17th International Conference on Pattern Recognition.Cambridge,UK,2004:430-433.
[121]JIN W,TUNG A K H,HAN J,et al.Ranking outliers using symmetric neighborhood relationship[C]//The Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining.Berlin:Springer,2006:577-593.
[122]TANG J,CHEN Z,FU A W C,et al.Enhancing effectiveness of outlier detections for low density patterns[C]//The Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining.Berlin:Springer,2002:535-548.
[123]HE Z,XU X,DENG S.Discovering cluster-based local outliers[J].Pattern Recognition Letters,2003,24(9/10):1641-1650.
[124]AMINANTO M E,KIM H J,KIM K M,et al.Another fuzzy anomaly detection system based on ant clustering algorithm[J].IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences,2017,100(1):176-183.
[125]BASAK J,KRISHNAPURAM R.Interpretable hierarchicalclustering by constructing an unsupervised decision tree[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(1):121-132.
[126]LIU F T,TING K M,ZHOU Z H.On detecting clustered ano-malies using sciforest[C]//The Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Berlin:Springer,2010:274-290.
[127]LIU Q Q,ZHANG J Y,CHEN B.Study on life prediction of radar based on non-parametric regression model[C]//The Proceedings of 2017 13th IEEE International Conference on Electronic Measurement & Instruments.Yangzhou,China,2017:586-590.
[128]HOU X,YANG J,DENG B,et al.A key lifetime parameters extraction method for T/R module based on association rule[C]//The Proceedings of 2018 International Conference on Sensing,Diagnostics,Prognostics,and Control.Xi'an,China,2018:165-170.
[129]DAI Z,YAO L,QIN J.Research on fault prediction of radarelectronic components based on analytic hierarchy process and BP neural network[C]//The Proceedings of 2019 12th Interna-tional Conference on Intelligent Computation Technology and Automation.Xiangtan,China,2019:91-95.
[130]LI Q,LV Y.Radar transmitting power supply health monitoring based on circuit modeling and simulation technology[C]//The Proceedings of IEEE International Conference on Power,Intelligent Computing and Systems.Shenyang,China,2019:584-588.
[131]LI W,ZHOU W,WANG Y M,et al.Meteorological radar fault diagnosis based on deep learning[C]//The Proceedings of 2019 International Conference on Meteorology Observations.Chengdu,China,2019:1-4.
[132]ZHANG T,CHEN J,CHEN X.Array diagnosis using signal subspace clustering in MIMO radar[J].Electronics Letters,2020,56(2):99-102.
[133]ZHAI Y,FANG S.A degradation fault prognostic method of radar transmitter combining multivariate long short-term memory network and multivariate gaussian distribution[J].IEEE Access,2020,8:199781-199791.
[134]CHEN J,ZHUO Q,LI J,et al.Array diagnosis and angle estimation in bistatic MIMO radar under array antenna failures[J].IET Radar,Sonar & Navigation,2019,13(7):1180-1188.
[135]KHAN S U,RAHIM M K A,MURAD N A,et al.Detection of the faulty sensors on basis of the pattern using symmetrical structure of linear array antenna[J].Applied Computational Electromagnetics Society Journal,2017,32(4):358-365.
[136]FINCHERA D,MIGLIORE M D,LUCIDO M,et al.Online fai-lure detection in large massive MIMO linear arrays[C]//The Proceedings of 2017 International Applied Computational Electromagnetics Society Symposium-Italy.Firenze,Italy,2017:1-2.
[137]YU Z,LEI Z,WEI P.Radar fault diagnosis based on chaos genetic reduction algorithm[C]//The Proceedings of 27th Chinese Control and Decision Conference.Qingdao,China,2015:1376-1381.
[138]XIAN M S,HE Y Y,PENG Z.Fault diagnosis expert system of artillery radar based on neural network[C]//The Proceedings of 2010 International Conference on Computer Design and Applications.Qinhuangdao,China,2010:V2-426-V2-429.
[139]LI Z H,WANG C J.A novel approach to architecture of radar fault diagnosis system based on mobile agents[C]//The Proceedings of 2010 8th World Congress on Intelligent Control and Automation.Jinan,China,2010:4520-4524.
[140]ZHANG X,SHENG W.Design and implementation of an embedded diagnosis system for radar built-in test equipment[C]//The Proceedings of 2010 International Symposium on Intelligence Information Processing and Trusted Computing.Huanggang,China,2010:173-176.
[141]XIE G,YAN S,TANG Z,et al.Fault diagnosis platform for radar circuit based on virtual instrument[C]//The Proceedings of 2010 International Conference on Measuring Technology and Mechatronics Automation.Changsha,China,2010:245-248.
[142]PAN W,XU J H,LIU S L.Radar fault diagnosis based on adaptive genetic algorithm and neural network[C]//The Proceedings of 2011 Chinese Control and Decision Conference.Mianyang,China,2011:3084-3088.
[143]BAI L,DU C,GUO Y.A fuzzy fault diagnosis method for large radar based on directed graph model[J].Journal of Shanghai Jiaotong University(Science),2015,20(3):363-369.
[144]JIE Z Z,YONG L Y,SU T C.Proposal for the software development of a radar power fault detection system[J].Physics Procedia,2012,33:877-883.
[145]OU Y X,YANG H,ZHU D J,et al.Research on technology of fault prediction for monopulse radar based on ARIMA and neural network[J].Computer Measurement & Control,2021,29(10):83-87.
[146]JIA Q Q,YANG G,CAO Y,et al.Research on a modelingmethod of radar fault prognosis model[J].Radar & ECM,2021,41(3):37-39.
[147]ZENG B,YU L A,LIU S F,et al.Unification of grey accumulation operator and the inverse operator and its application in Radar fault prediction[J].System Engineering Theory and Practice,2021,41(10):2710-2720.
[148]LIU J,SHAO W.Study of prognostics and health management technology for airborne radar[J].Computer Knowledge and Technology,2017,13(15):190-191.
[149]WANG W F,JIA X L,HUANG L M.Research on radar intelligent BIT fault prediction system technology[J].Management & Technology of SME,2016(9):182-183.
[150]SHAO Y J,MA C M,PAN H X.Analysis of radar fault prediction based on combined mode[J].Journal of Measurement Science and Instrumentation,2016,7(1):44-47.
[151]XU S J,TAN X S,WANG H Z,et al.A MUGM(1,m,w) model of radar fault prediction[J].Modern Radar,2011,33(8):25-28.
[152]ZHOU J J,WANG D G,CHANG S.Radar fault predictionbased on improved GM(1,1) model[J].Equipment technology,2010(3):16-18.
[153]WANG H Z,ZOU J H,LIU F.Application of the improved un-equal interval grey model to radar fault forecast[J].Journal of Air Force Early Warning Academy,2009,23(5):321-323.
[154]LI H.Research on the radar fault diagnosis method based on fault tree[J].Wireless Internet Technology,2019,16(1):3-4.
[155]HUANG Y C,GUO Z Y.CINRAD/SA weather radar faultdiagnosis system based on fault tree analysis[J].Meteorological Science and Technology,2017,45(5):930-937.
[156]WANG F.Radar fault diagnosis method based on fault tree[J].Informatization Research,2016,42(3):59-62.
[157]ZHUANG X.Fault diagnosis method for radar based on reinforcement learning neural network[J].Modern Radar,2017,39(12):15-19.
[158]ZHOU Q,ZHAO S W,WEN W J.Fault diagnosis of radarbased on BP network[J].Fire Control Radar Technology,2005 (2):77-79.
[159]JIANG Y Y.Application of BP neural network in radar fault diagnosis[J].Telecom Power Technologies,2020,37(6):119-120.
[160]HU X N,SHI Z K.Application research of BP neural network in fault diagnosis of radar[J].Computer Measurement & Control,2006,(12):1660-1662.
[161]WANG X J,CHEN C X,ZHAO X J,et al.Improved LS-SVM based radar fault diagnosis technology[J].Fire Control & Command Control,2015,40(2):63-65,69.
[162]TU W M,SONG Z H,CHEN Y T,et al.Radar fault diagnosis based on wavelet transformation and LS-SVM[J].Control Engineering of China,2013,20(2):309-312.
[163]TU W M,CHEN Y T,WEI Y G.PCB fault diagnosis system based on OWPB and LS-SVM[J].Application of Electronic Technique,2012,38(7):132-134,137.

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