参考文献/References:
[1] 施鹏飞. 中国风电产业发展现状和展望[C]. 国际清洁能源论坛(澳门): 国际清洁能源论坛(澳门)秘书处,2019.
[2] Stadler K, Stubenrauch A. Premature bearing failures in industrial gearboxes[J]. SKF, 2013, 12: 97421.
[3] Renewable Energy World Magazine. Analyzing gearbox failure and preventing it[EB/OL]. https://www.researchgate. net/publication/225602159, 2015-03-02.
[4] 封新建. 风力发电机组齿轮箱振动监测与故障诊断方法研究[D]. 吉林: 东北电力大学,2017.
[5] Tian Z, Jin T, Wu B, et al. Condition based maintenance optimization for wind power generation systems under continuous monitoring[J]. Renewable Energy, 2011, 36: 1502-1509.
[6] Elforjani M, Mba D. Condition monitoring of slow-speed shafts and bearings with acoustic emission[M]. Oxford: Blackwell Publishing, 2010.
[7] Eftekharnejad B, Carrasco M R, Charnley B, et al. The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing[J]. Mechanical Systems and Signal Processing, 2011, 25: 266-284.
[8] Kilundu B, Chiementin X, Duez J, et al. Cyclostationarity of acoustic emissions (AE) for monitoring bearing defects[J]. Mechanical Systems and Signal Processing, 2011, 25: 2061-2072.
[9] Renaudin L, Bonnardot F, Musy O, et al. Natural roller bearing fault detection by angular measurement of true instantaneous angular speed[J]. Mechanical Systems and Signal Processing, 2010, 24(7): 1998-2011.
[10] Holweger W, Walther F, Loos J, et al. Nondestructive subsurface damage monitoring in bearings failure mode using fractal dimension analysis[J]. Industrial Lubrication and Tribology, 2012, 64: 132-137.
[11] Machado C, Guessasma M, Bellenger E, et al. Diagnosis of faults in the bearings by electrical measures and numerical simulations[J]. Mechanics Industry, 2014, 15(5): 383-391.
[12] Kim S Y, Ra I H, Kim S H. Design of wind turbine fault detection system based on performance curve SCIS-ISIS[C]. The 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced Intelligence Systems, IEEE, 2013.
[13] P.eng G. Wind Turbine Generator Bearing Condition Monitoring with NSET Method[C]. Control and Decision Conference, 2012.
[14] Yang W, Court R, Jiang J. Wind turbine condition monitoring by the approach of SCADA data analysis[J]. Renew Energy, 2013, 53: e365-e376.
[15] Dupuis R. Application of oil debris monitoring for wind turbine gearbox prognostics and health management[C]. Annual Conference of the Prognostics and Health Management Society, 2010.
[16] Jiang X, Liu F, Zhao P. Failure analysis of rolling bearing based on oil monitoring techniques with mechanics basis[J]. Applied Mechanics and Materials, 2012, 164: 401-404.
[17] Miao Q, Cong L, Pecht M. Identifification of multiple characteristic components with high accuracy and resolution using the zoom interpolated discrete Fourier transform[J]. Measurement Science and Technology, 2011, 22(5): 055701.
[18] Jayaswal P, Agrawal B. New trends in wind turbine condition monitoring system[J]. International Journal of Emerging Trends in Engineering Research, 2011, 3(1): 133-148.
[19] Liu T. Lee J, Singh P. Using acceleration measurements and neurofuzzy systems for monitoring and diagnosis of bearings[J]. International Society for Optics and Photonics, 2013, 8916: 89160B.
[20] Saidi L, Ali JB, Fnaiech F. Application of higher order spectral features and support vector machines for bearing faults classifification[J]. Isa Transactions, 2015, 54: 193-206.
[21] Sarvajith M, Shah B, Kulkarni S, et al. Condition monitoring of rolling element bearing using wavelet transform and support vector machine[C]. Conference: NCCM, 2013.
[22] Khanam S, Tandon N, Dutt J K. Fault size estimation in the outer race of ball bearing using discrete wavelet transform of the vibration signal[J]. Procedia Technology, 2014, 14: 12-19.
[23] Ali J B, Fnaiech N, Saidi L, et al. Application of empirical mode decomposition and artifificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics, 2015, 89: 16-27.
[24] Ming A B, Zhangb W, Qina Z Y, et al. Envelope calculation of the multi component signal and its application to the deterministic component can cellation in bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2015, 50: 70-100.
[25] Fu W, Shao K, Tan J, et al. Fault diagnosis for rolling bearings based on composite multiscale fine-sorted dispersion entropy and SVM with hybrid mutation SCA-HHO algorithm optimization[J]. IEEE Access, 2020, 99: 1.
[26] Gelle G , Colas M , Serviere C. Blind source separation: a tool for rotating machine monitoring by vibrations analysis[J]. Journal of Sound and Vibration, 2001, 248(5): 865-885.
[27] Roan M J , Erling J G , Sibul L H. A new, non-linear, adaptive, blind source separation approach to gear tooth failure detection and analysis[J]. Mechanical Systems and Signal Processing, 2002, 16(5): 719-740.
[28] Hu C, Yang Q, Huang M, et al. Sparse component analysis-based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox[J]. IET Renewable Power Generation, 2017, 11(3): 330-337.
[29] Zhang Y, Qi S, Zhou L. Single channel blind source separation for wind turbine aeroacoustics signals based on variational mode decomposition[J]. Ieee Access, 2018, 6: 73952-73964
[30] Ziani R, Zagadi R, Felkaoui A, et al. Bearing fault diagnosis using neural network and genetic algorithms with the trace criterion[J]. Condition Monitoring of Machinery in Non-Stationary Operations, 2012, 37: 89-96.
[31] Xu J, Huang J, Zhao Y, et al. A robust intelligent fault diagnosis method for rolling bearings based on deep convolutional neural network and domain adaptation[J]. Procedia Computer Science, 2020, 174: 400-405.
[32] Zhao B, Zhang X, Zhan Z, et al. Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains[J]. Neurocomputing, 2020, 407: 24-38.
[33] Li Y, Jiang W, Zhang G, et al. Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data[J]. Renewable Energy, 2021, 171: 103-115.
[34] Yu W X, Lu Y, Wang J N. Application of small sample virtual expansion and spherical mapping model in wind turbine fault diagnosis[J]. Expert Systems with Applications, 2021, 14: 115397.
[35] Wen X, Xu Z. Wind turbine fault diagnosis based on ReliefF-PCA and DNN[J]. Expert Systems with Applications, 2021, 178: 115016.