[1] Yeping Peng, JunhaoCai, Tonghai Wu, Gugangzhong Cao, Ngaiming Kwok, Shengxi Zhou, Zhongxiao Peng, "Online wear characterisation of rolling element bearing using wear particle morphological features", Wear, 2019: 430–431, p. 369-375.
[2] Wontae KIM, Jinju SEO, Dongpyp HONG. "Infrared Thermographic Inspection of Ball Bearing; Condition Monitoring for Defects under Dynamic Loading Stages", 18
th World conference on Nondestructive Testing, 16-20 April 2012, Durban, South Africa.
[5] Bakhtiarinejad F., H. Ali, S. Ehsani,Bearingdiagnoise via vibration analysis, 13th conference on Mechanical engineering, 2004, Isfahan, Iran.
[7] Jaouher Ben Ali, Nader Fnaiech, LotfiSaidi, Brigitte Chebel-Morello, FarhatFnaiech, "Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals", Applied Acoustics, 2015.89: p. 16-27.
[8] Mohammad Ali Farsi, S. Masood Hosseini. "Statistical distributions comparison for remaining useful life prediction of components via ANN",Int J Syst Assur Eng Manag2019. 10: p.429–436. doi:10.1007/s13198-019-00813-w.
[9] Sawaqed, L.S., Alrayes, A.M. "Bearing fault diagnostic using machine learning algorithms". ProgArtif Intell.2020.
9: p.341–350
doi: 10. 1007/s13748-020-00217-z.
[10] Duy-Tang Hoang, Hee-Jun Kang, "A survey on Deep Learning based bearing fault diagnosis",Neuro computing, 2019. 335: p. 327-335.
[11] Shen Zhang, Shibo Zhang, "Bingnan Wang, Thomas G. Habetler, Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review", IEEE Access, 2020.8:p. 29857-29881, doi: 10.1109/ACCESS.2020.2972859.
[12] Lu W, Liang B, Cheng Y, Meng D, Yang J, Zhang T. "Deep Model Based Domain Adaptation for Fault Diagnosis". IEEE Trans Ind Electron; 2017, 64: p.2296–305. 2017, doi:10.1109/TIE.2016.2627020.
[13] M. Elforjani and S. Shanbr, “Prognosis of bearing acoustic emissionsignals using supervised machine learning,” IEEE Trans. Ind. Electron., 2018. 65(7): p. 5864–5871.
[14] C. Lu, Z.-Y Wang, W.-L. Qin, J. Ma, “Fault diagnosis of rotarymachinery components using a stacked denoisingautoencoder-basedhealth state identification,” Signal Process., 2017. 130: p. 377–388.
[15] Nectoux, R. Gouriveau, K. Medjaher, E. Ramasso, B. Morello, N. Zerhouni, C. Varnier. PRONOSTIA: An Experimental Platform for Bearings Accelerated Life Test. IEEE International Conference on Prognostics and Health Management, Denver, CO, USA, 2012.
[18] MihirMody and Kumar Desappan and P. Swami and M. Mathew and S. Nagori."Low cost and power CNN/deep learning solution for automated driving", 19th International Symposium on Quality Electronic Design (ISQED), 2018.P. 432-436.