留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Prognostics and health management for electromechanical system: A review

Dong LIU Jian SHI Zirui LIAO Haoyu GUO

Dong LIU, Jian SHI, Zirui LIAO, Haoyu GUO. Prognostics and health management for electromechanical system: A review[J]. 先进制造科学与技术, 2022, 2(4): 2022015. doi: 10.51393/j.jamst.2022015
引用本文: Dong LIU, Jian SHI, Zirui LIAO, Haoyu GUO. Prognostics and health management for electromechanical system: A review[J]. 先进制造科学与技术, 2022, 2(4): 2022015. doi: 10.51393/j.jamst.2022015
Dong LIU, Jian SHI, Zirui LIAO, Haoyu GUO. Prognostics and health management for electromechanical system: A review[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(4): 2022015. doi: 10.51393/j.jamst.2022015
Citation: Dong LIU, Jian SHI, Zirui LIAO, Haoyu GUO. Prognostics and health management for electromechanical system: A review[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(4): 2022015. doi: 10.51393/j.jamst.2022015

Prognostics and health management for electromechanical system: A review

doi: 10.51393/j.jamst.2022015
基金项目: 

This paper was co-supported by the National Natural Science Foundation of China (Nos. 51875014 and 51875015) and the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (No. 51620105010).

详细信息
    通讯作者:

    Jian SHI,E-mail:shijian@buaa.edu.cn

Prognostics and health management for electromechanical system: A review

Funds: 

This paper was co-supported by the National Natural Science Foundation of China (Nos. 51875014 and 51875015) and the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (No. 51620105010).

  • 摘要:

    As a transmission component, gears take on a great significance for the Electromechanical system of aviation equipment and has long aroused the widespread attention of researchers. Fault diagnosis and remaining useful life (RUL) prediction during the gear operation is critical to prognostics and health management (PHM) of gear transmission systems. In this paper, the focus is placed on gear PHM methods. This paper attempts to review the existing methods and summarize them into four types (including physical model-based, knowledge model-based, data-driven model-based, as well as hybrid model-based methods). Based on a wide variety of methods, the principle and the application situation are indicated. In particular, the data-driven model-based methods consist of stochastic algorithms, statistical algorithms, as well as the artificial intelligence (AI) method. The fault diagnosis, performance degradation and RUL prediction of various methods are primarily summarized. Furthermore, the advantages and disadvantages of various methods are assessed, and the prospect of the Digital Twin (DT) is forecasted to boost the applications of PHM.

  • [1] . Nejad AR, Guo Y, Gao Z, et al. Development of a 5 MW reference gearbox for offshore wind turbines. Wind Energ 2016;19(6):1089-1106.
    [2] . Schneider T, Kruse T, Kuester B, et al. Evaluation of an energy self-sufficient sensor for monitoring marine gearboxes. Procedia Manuf 2018;24:135-140.
    [3] . Garza P, Perinpanayagam S, Aslam S, et al. Qualitative validation approach using digital model for the health management of electromechanical actuators. Appl Sci 2020;10(21):7809.
    [4] . Balaban E, Bansal P, Stoelting P, et al. A diagnostic approach for electro-mechanical actuators in aerospace systems. 2009 IEEE Aerospace conference. March 7-14, 2009, Big Sky, MT, USA. IEEE, 2009:1-13.
    [5] . Gao R, Wang L, Teti R, et al. Cloud-enabled prognosis for manufacturing. CIRP Ann 2015;64(2):749-772.
    [6] . Lei YG, Yang B, Jiang XW, et al. Applications of machine learning to machine fault diagnosis:a review and roadmap. Mech Syst Signal Process 2020;138:106587.
    [7] . Zhao R, Yan RQ, Chen ZH, et al. Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 2019;115:213-237.
    [8] . Liu RN, Yang BY, Zio E, et al. Artificial intelligence for fault diagnosis of rotating machinery:a review. Mech Syst Signal Process 2018;108:33-47.
    [9] . Li WH, Huang RY, Li JP, et al. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios:Theories, applications and challenges. Mech Syst Signal Process 2022;167:108487.
    [10] . Lei YG, Li NP, Guo L, et al. Machinery health prognostics:a systematic review from data acquisition to RUL prediction. Mech Syst Signal Process 2018;104:799-834.
    [11] . He B, Liu L, Zhang D. Digital twin-driven remaining useful life prediction for gear performance degradation:a review. J Comput Inf Sci Eng 2021;21(3):030801.
    [12] . Ochella S, Shafiee M, Dinmohammadi F. Artificial intelligence in prognostics and health management of engineering systems. Eng Appl Artif Intell 2022;108:104552.
    [13] . Tinga T, Loendersloot R. Physical model-based prognostics and health monitoring to enable predictive maintenance. Predict Maintenance Dyn Syst 2019:313-353.
    [14] . Armendia M, Alzaga A, Peysson F, et al. Twin-control approach. Twin-Control 2019:23-38.
    [15] . Schluse M, Priggemeyer M, Atorf L, et al. Experimentable digital twins-streamlining simulation-based systems engineering for industry 4.0. IEEE Trans Ind Inform 2018;14(4):1722-1731.
    [16] . British Standard BS 7848:1996 (ISO 10825:1995). GEARS-Wear and Damage to Gear Teeth-Terminology.
    [17] . Luo JH, Bixby A, Pattipati K, et al. An interacting multiple model approach to model-based prognostics. SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No.03CH37483). October 8-8, 2003, Washington, DC, USA. IEEE, 2003:189-194.
    [18] . Djurdjanovic D, Lee J, Ni J. Watchdog Agent-an infotronics-based prognostics approach for product performance degradation assessment and prediction. Adv Eng Inform 2003;17(3-4):109-125.
    [19] . Guo L, Chen J. A Review on machinery performance degradation assessment and prediction. Journal of Vibration and Shock 2008; 27(S):67-70.
    [20] . Wang H, Ma HB, Xu HL, et al. Review on machinery performance degradation assessment and prognostics. J Mech Strength 2013;35(6):716-723[Chinese].
    [21] . Wu SY, Zuo MJ, Parey A. Simulation of spur gear dynamics and estimation of fault growth. J Sound Vib 2008;317(3-5):608-624.
    [22] . Xu ZF, Shao RP. Forecast of sound pressure level of gear systems and fault diagnosis based on acoustics. Comput Meas Control 2009;17(9):1688-1691[Chinese].
    [23] . Amarnath M, Lee SK. Assessment of surface contact fatigue failure in a spur geared system based on the tribological and vibration parameter analysis. Measurement 2015;76:32-44.
    [24] . Wan X, Wang D, Tse PW, et al. A critical study of different dimensionality reduction methods for gear crack degradation assessment under different operating conditions. Measurement 2016;78:138-150.
    [25] . Qiu YN, Chen L, Feng YH, et al. An approach of quantifying gear fatigue life for wind turbine gearboxes using supervisory control and data acquisition data. Energies 2017;10(8):1084.
    [26] . Guilbault R, Lalonde S. Early diagnostic of concurrent gear degradation processes progressing under time-varying loads. Mech Syst Signal Process 2016;76-77:337-352.
    [27] . Feng P, Borghesani P, Chang H, et al. Monitoring gear surface degradation using cyclostationarity of acoustic emission. Mech Syst Signal Process 2019;131:199-221.
    [28] . Kundu P, Darpe AK, Kulkarni MS. Gear pitting severity level identification using binary segmentation methodology. Struct Control Health Monit 2020;27(3):e2478.
    [29] . Tao F, Zhang M, Liu YS, et al. Digital twin driven prognostics and health management for complex equipment. CIRP Ann 2018;67(1):169-172.
    [30] . Thirumurugan R, Gnanasekar N. Influence of finite element model, load-sharing and load distribution on crack propagation path in spur gear drive. Eng Fail Anal 2020;110:104383.
    [31] . Forman RG, Kearney VE, Engle RM. Numerical analysis of crack propagation in cyclic-loaded structures. ASME J Basic Eng 1967; 89(3):459-463.
    [32] . Rosenfeld MS. Effects of Environment and complex load history on fatigue life. 1970.
    [33] Rosenfeld MS. Damage tolerance in aircraft structures. 1971.
    [34] . Ritchie RO, Knott JF. Mechanisms of fatigue crack growth in low alloy steel. Acta Metall 1973;21(5):639-648.
    [35] . Allen R, Booth G, Jutla T. A review of fatigue crack growth characterization by linear elastic fracture mechanics (LEFM). Part I-Principles and methods of data generation. Fatigue Fract Eng Mater Struct 2007;11(1):45-69.
    [36] . Ritchie RO. Mechanisms of fatigue-crack propagation in ductile and brittle solids. Int J Fract 1999;100(1):55-83.
    [37] . Forman R, Shivakumar V, Cardinal J, et al. Fatigue crack growth database for damage tolerance analysis[Internet]. 2005.Available from:https://ntrs.nasa.gov/search.jsp?R=20050232857.
    [38] . Vullo V. Gears. 2020. p. 73-147.
    [39] . Wang W, Liu HJ, Zhu CC, et al. Effects of microstructure on rolling contact fatigue of a wind turbine gear based on crystal plasticity modeling. Int J Fatigue 2019;120:73-86.
    [40] . Liu HJ, Liu HL, Zhu CC, et al. A review on micropitting studies of steel gears. Coatings 2019;9(1):42.
    [41] . Janaswamy PK, Chowdary JR, Sasanka CT, et al. Life prediction of spur gear under fully reversed loading using total life approach and crack-initiation method in FEM. Aksaray Univ J Sci Eng 2019:498344.
    [42] . Miner MA. Cumulative damage in fatigue. Transactions of the ASCE 2022;67:A159-A164.
    [43] . Hanumanna D, Narayanan S, Krishnamurthy S. Prediction of fatigue life of gear subjected to varying loads. Def Sci J 1998;48(3):277-285.
    [44] . Deng HL, Li W, Sakai T, et al. Very high cycle fatigue failure analysis and life prediction of Cr-Ni-W gear steel based on crack initiation and growth behaviors. Materials 2015;8(12):8338-8354.
    [45] . Shen HD, Li ZQ, Qi LL, et al. A method for gear fatigue life prediction considering the internal flow field of the gear pump. Mech Syst Signal Process 2018;99:921-929.
    [46] . Jia P, Liu HJ, Zhu CC, et al. Contact fatigue life prediction of a bevel gear under spectrum loading. Front Mech Eng 2020;15(1):123-132.
    [47] . Bektas O, Marshall J, Jones JA. Comparison of computational prognostic methods for complex systems under dynamic regimes:a review of perspectives. Arch Comput Methods Eng 2020;27(4):999-1011.
    [48] . Krishnamurthi M, Phillips DT. An expert system framework for machine fault diagnosis. Comput Ind Eng 1992;22(1):67-84.
    [49] . Gelgele HL, Wang KS. An expert system for engine fault diagnosis:Development and application. Journal of Intelligent Manufacturing 1998; 9:539-545.
    [50] . Angeli C. An online expert system for fault diagnosis in hydraulic systems. Expert Syst 1999;16(2):115-120.
    [51] . White MF. Expert systems for fault diagnosis of machinery. Measurement 1991;9(4):163-171.
    [52] . Sahin S, Tolun MR, Hassanpour R. Hybrid expert systems:a survey of current approaches and applications. Expert Syst Appl 2012;39(4):4609-4617.
    [53] . Yang ZL, Wang B, Dong XH, et al. Expert system of fault diagnosis for gear box in wind turbine. Syst Eng Procedia 2012;4:189-195.
    [54] . Aberšek B, Flašker J, Balič J. Expert system for designing and manufacturing of a gear box. Expert Syst Appl 1996;11(3):397-405.
    [55] . Su YL, Lin JS, Hsieh SK. The tribological failure diagnosis of spur gear by an expert system. Wear 1993;166(2):187-196.
    [56] . Chan YW, Sim SK. A knowledge-based expert system for gearing design application using Prolog and C. Adv Eng Softw 1994;19(3):149-159.
    [57] . Lei YG, Lin J, Zuo MJ, et al. Condition monitoring and fault diagnosis of planetary gearboxes:a review. Measurement 2014;48:292-305.
    [58] . Goyal D, Vanraj, Pabla BS, et al. Condition monitoring parameters for fault diagnosis of fixed axis gearbox:a review. Arch Comput Methods Eng 2017;24(3):543-556.
    [59] . Li C, Sanchez RV, Zurita G, et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mech Syst Signal Process 2016;76-77:283-293.
    [60] . Wong PK, Zhong JH, Yang ZX, et al. A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine. Proc Inst Mech Eng C J Mech Eng Sci 2017;231(6):1146-1161.
    [61] . Yoon J, He D. Planetary gearbox fault diagnostic method using acoustic emission sensors. IET Sci Meas Technol 2015;9(8):936-944.
    [62] . Duan ZH, Wu TH, Guo SW, et al. Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings:a review. Int J Adv Manuf Technol 2018;96(1-4):803-819.
    [63] . Zhang ZX, Si XS, Hu CH, et al. Degradation data analysis and remaining useful life estimation:a review on Wiener-process-based methods. Eur J Oper Res 2018;271(3):775-796.
    [64] . Naji LF, Rasheed HA. Bayesian estimation for two parameters of gamma distribution under generalized weighted loss function. Eijs 2019;60(5):1161-1171.
    [65] . Wang XJ, Lin SR, Wang SP, et al. Remaining useful life prediction based on the Wiener process for an aviation axial piston pump. Chin J Aeronaut 2016;29(3):779-788.
    [66] . Xu X, Yu C, Tang S, et al. Remaining useful life prediction of lithium-ion batteries based on wiener processes with considering the relaxation effect. Energies 2019; 12(9):1685.
    [67] . Si XS, Wang WB, Hu CH, et al. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mech Syst Signal Process 2013; 35(1-2):219-237.
    [68] . Ni XL, Zhang X, Sun FC, et al. An adaptive state-space model for predicting remaining useful life of planetary gearbox. 2016 Prognostics and System Health Management Conference (PHM-Chengdu). October 19-21, 2016, Chengdu, China. IEEE, 2016.p.1-6.
    [69] . Zhang YB, Zhao XH, Liu W, et al. Research on gearbox wearing prognosis based on Gamma-State Space Model. The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety. June 12-15, 2011, Guiyang, China. IEEE, 2011.p.279-283.
    [70] . Peng WW, Li YF, Mi JH, et al. Reliability of complex systems under dynamic conditions:a Bayesian multivariate degradation perspective. Reliab Eng Syst Saf 2016;153:75-87.
    [71] . Ke XJ, Xu ZG. A model for degradation prediction with change point based on Wiener process. 2015 IEEE International Conference on Industrial Engineering and Engineering Management. December 6-9, 2015, Singapore. IEEE, 2015.p.986-990.
    [72] . Paroissin C. Inference for the Wiener process with random initiation time. IEEE Trans Reliab 2016;65(1):147-157.
    [73] . Yuan HD, Chen J, Dong GM. An improved initialization method of D-KSVD algorithm for bearing fault diagnosis. J Mech Sci Technol 2017;31(11):5161-5172.
    [74] . Yu J, He YJ. Planetary gearbox fault diagnosis based on data-driven valued characteristic multigranulation model with incomplete diagnostic information. J Sound Vib 2018;429:63-77.
    [75] . Yu J, Huang WT, Zhao XZ. Combined flow graphs and normal naive Bayesian classifier for fault diagnosis of gear box. Proc Inst Mech Eng C J Mech Eng Sci 2016;230(2):303-313.
    [76] . Yu J, Bai MY, Wang GN, et al. Fault diagnosis of planetary gearbox with incomplete information using assignment reduction and flexible naive Bayesian classifier. J Mech Sci Technol 2018;32(1):37-47.
    [77] . He YL, Wang R, Kwong S, et al. Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis. Inf Sci 2014;259:252-268.
    [78] . Zheng RS, Dong XM, Hao WS, et al. Application of hidden Markov models in ball mill gearbox for fault diagnosis. Adv Mater Res 2013;842:401-4.
    [79] . Jia YX, Sun L, Teng HZ. A comparison study of hidden Markov model and particle filtering method:application to fault diagnosis for gearbox. Proceedings of the IEEE 2012 Prognostics and System Health Management Conference. May 23-25, 2012, Beijing. IEEE, 2012.p.1-7.
    [80] . Wang D, Miao Q, Zhou QH, et al. An intelligent prognostic system for gear performance degradation assessment and remaining useful life estimation. J Vib Acoust 2015;137(2):021004.
    [81] . He B, Zhu XR, Zhang D. Boundary encryption-based Monte Carlo learning method for workspace modeling. J Comput Inf Sci Eng 2020;20(3):034502.
    [82] . Joshuva A, Sugumaran V, Amarnath M, et al. Remaining life-time assessment of gear box using regression model. Indian J Sci Technol 2016;9(47):1-8.
    [83] . Nanadic N, Ardis P, Hood A, et al. Comparative study of vibration condition indicators for detecting cracks in spur gears[Internet]. 2013 Available from:https://ntrs.nasa.gov/citations/20130014045.
    [84] . Wang WY, Wong AK. Autoregressive model-based gear fault diagnosis. J Vib Acoust 2002;124(2):172-179.
    [85] . Assaad B, Eltabach M, Antoni J. Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes. Mech Syst Signal Process 2014;42(1-2):351-367.
    [86] . Liu ZL, Zuo MJ, Xu HB. Feature ranking for support vector machine classification and its application to machinery fault diagnosis. Proc Inst Mech Eng C J Mech Eng Sci 2013;227(9):2077-2089.
    [87] . Li C, Sanchez RV, Zurita G, et al. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 2015;168:119-127.
    [88] . Lu WB, Jiang WK, Yuan GQ, et al. A gearbox fault diagnosis scheme based on near-field acoustic holography and spatial distribution features of sound field. J Sound Vib 2013;332(10):2593-2610.
    [89] . Cheng FZ, Peng YY, Qu LY, et al. Current-based fault detection and identification for wind turbine drivetrain gearboxes. IEEE Trans Ind Appl 2017;53(2):878-887.
    [90] . Xing ZQ, Qu JF, Chai Y, et al. Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine. J Mech Sci Technol 2017;31(2):545-553.
    [91] . Liu LB, Liang XH, Zuo MJ. A dependence-based feature vector and its application on planetary gearbox fault classification. J Sound Vib 2018;431:192-211.
    [92] . Shen CQ, Wang D, Kong FR, et al. Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement 2013;46(4):1551-1564.
    [93] . Bordoloi DJ, Tiwari R. Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time-frequency vibration data. Measurement 2014;55:1-14.
    [94] . Zhang X, Zhao JM, Zhang XH, et al. A novel hybrid compound fault pattern identification method for gearbox based on NIC, MFDFA and WOASVM. J Mech Sci Technol 2019;33(3):1097-1113.
    [95] . Widodo A, Yang BS. Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 2007;21(6):2560-2674.
    [96] . Abu-Mahfouz IA. A comparative study of three artificial neural networks for the detection and classification of gear faults. Int J Gen Syst 2005;34(3):261-277.
    [97] . Rafiee J, Tse PW, Harifi A, et al. A novel technique for selecting mother wavelet function using an intelli Gent fault diagnosis system. Expert Syst Appl 2009;36(3):4862-4875.
    [98] . Hajnayeb A, Ghasemloonia A, Khadem SE, et al. Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis. Expert Syst Appl 2011;38(8):10205.
    [99] . Cerrada M, Vinicio Sánchez R, Cabrera D, et al. Multi-stage feature selection by using genetic algorithms for fault diagnosis in gearboxes based on vibration signal. Sens Basel Switz 2015;15(9):23903.
    [100] . Kane PV, Andhare AB. Application of psychoacoustics for gear fault diagnosis using artificial neural network. J Low Freq Noise Vib Active Control 2016;35(3):207-220.
    [101] . Waqar T, Demetgul M. Thermal analysis MLP neural network based fault diagnosis on worm gears. Measurement 2016;86:56-66.
    [102] . Tyagi S, Panigrahi SK. A hybrid genetic algorithm and back-propagation classifier for gearbox fault diagnosis. Appl Artif Intell 2017;31(7-8):593-612.
    [103] . Lai WX, Tse PW, Zhang GC, et al. Classification of gear faults using cumulants and the radial basis function network. Mech Syst Signal Process 2004;18(2):381-389.
    [104] . Li H, Zhang YP, Zheng HQ. Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network. J Mech Sci Technol 2009;23(10):2780-2789.
    [105] . Liu HM, Zhang JC, Cheng YJ, et al. Fault diagnosis of gearbox using empirical mode decomposition and multi-fractal detrended cross-correlation analysis. J Sound Vib 2016;385:350-371.
    [106] . Chen HX, Lu YJ, Tu L. Fault identification of gearbox degradation with optimized wavelet neural network. Shock Vib 2013;20:598490.
    [107] . Gharavian MH, Ganj FA, Ohadi AR, et al. Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gearboxes. Neurocomputing 2013;121:150-159.
    [108] . Li ZX, Yan XP, Tian Z, et al. Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis. Measurement 2013;46(1):259-271.
    [109] . Park S, Kim S, Choi JH. Gear fault diagnosis using transmission error and ensemble empirical mode decomposition. Mech Syst Signal Process 2018;108:262-275.
    [110] . Vanraj, Dhami SS, Pabla BS. Hybrid data fusion approach for fault diagnosis of fixed-axis gearbox. Struct Heal Monit 2018;17(4):936-945.
    [111] . Praveenkumar T, Sabhrish B, Saimurugan M, et al. Pattern recognition based on-line vibration monitoring system for fault diagnosis of automobile gearbox. Measurement 2018;114:233-242.
    [112] . Li C, Sanchez RV, Zurita G, et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mech Syst Signal Process 2016;76-77:283-93.
    [113] . Yin JT, Zhao WT. Fault diagnosis network design for vehicle on-board equipments of high-speed railway:a deep learning approach. Eng Appl Artif Intell 2016;56:250-259.
    [114] . He J, Yang SX, Gan CB. Unsupervised fault diagnosis of a gear transmission chain using a deep belief network. Sensors 2017;17(7):1564.
    [115] . Jing LY, Zhao M, Li P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 2017;111:1-10.
    [116] . Jiao JY, Zhao M, Lin J, et al. A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes. Knowl Based Syst 2018;160:237-250.
    [117] . Jing LY, Wang TY, Zhao M, et al. An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox. Sensors 2017;17(2):414.
    [118] . Han Y, Tang BP, Deng L. An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes. Comput Ind 2019;107:50-58.
    [119] . Yao Y, Wang HL, Li SB, et al. End-to-end convolutional neural network model for gear fault diagnosis based on sound signals. Appl Sci 2018;8(9):1584.
    [120] . Li XY, Li JL, Qu YZ, et al. Gear pitting fault diagnosis using integrated CNN and GRU network with both vibration and acoustic emission signals. Appl Sci 2019;9(4):768.
    [121] . Jiang GQ, He HB, Yan J, et al. Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Trans Ind Electron 2019;66(4):3196-3207.
    [122] . Zhao MH, Kang M, Tang BP, et al. Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes. IEEE Trans Ind Electron 2018;65(5):4290-4300.
    [123] . Zhao MH, Kang M, Tang BP, et al. Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis. IEEE Trans Ind Electron 2019;66(6):4696-4706.
    [124] . Ma S, Chu FL, Han QK. Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions. Mech Syst Signal Process 2019;127:190-201.
    [125] . Zhang W, Li CH, Peng GL, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process 2018;100:439-453.
    [126] . Yang B, Lei YG, Jia F, et al. A transfer learning method for intelligent fault diagnosis from laboratory machines to real-case machines. 2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC). August 15-17, 2018, Xi'an, China. IEEE, 2018.p.35-40.
    [127] . Pan SJ, Tsang IW, Kwok JT, et al. Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 2011;22(2):199-210.
    [128] . Chen C, Li ZH, Yang J, et al. A cross domain feature extraction method based on transfer component analysis for rolling bearing fault diagnosis. 2017 29th Chinese Control and Decision Conference (CCDC). May 28-30, 2017, Chongqing, China. IEEE, 2017.p.5622-6.
    [129] . Xie JY, Zhang LB, Duan LX, et al. On cross-domain feature fusion in gearbox fault diagnosis under various operating conditions based on Transfer Component Analysis. 2016 IEEE International Conference on Prognostics and Health Management. June 20-22, 2016, Ottawa, ON, Canada. IEEE, 2016.p.1-6.
    [130] . Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2010;22(10):1345-1359.
    [131] . Cao P, Zhang SL, Tang J. Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning. IEEE Access 2018;6:26241.
    [132] . Shao SY, McAleer S, Yan RQ, et al. Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Ind Inform 2019;15(4):2446-2455.
    [133] . Liao LX, Köttig F. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Trans Reliab 2014;63(1):191-207.
    [134] . Guo J, Li ZJ, Li MY. A review on prognostics methods for engineering systems. IEEE Trans Reliab 2020;69(3):1110-1129.
    [135] . Orchard ME, Vachtsevanos GJ. A particle filtering approach for on-line failure prognosis in a planetary carrier plate. Int J Fuzzy Log Intell Syst 2007;7(4):221-227.
    [136] . Chen CC, Vachtsevanos G, Orchard ME. Machine remaining useful life prediction:an integrated adaptive neuro-fuzzy and high-order particle filtering approach. Mech Syst Signal Process 2012;28:597-607.
    [137] . Liu J, Wang W, Ma F, et al. A data-model-fusion prognostic framework for dynamic system state forecasting. Eng Appl Artif Intell 2012;25(4):814-823.
    [138] . Lei YG, Li NP, Gontarz S, et al. A model-based method for remaining useful life prediction of machinery. IEEE Trans Reliab 2016;65(3):1314-1326.
    [139] . Li NP, Lei YG, Lin J, et al. An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Trans Ind Electron 2015;62(12):7762-7773.
  • 加载中
图(1)
计量
  • 文章访问数:  1011
  • HTML全文浏览量:  412
  • PDF下载量:  139
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-04-01
  • 录用日期:  2022-04-25
  • 修回日期:  2022-04-15
  • 网络出版日期:  2022-04-25
  • 刊出日期:  2022-06-06

目录

    /

    返回文章
    返回