TitleA smart fault identification system for ball bearing using simulation-driven vibration analysis
Journal titleArchive of Mechanical Engineering
AffiliationKhaire, Pallavi : Veermata Jijabai Technological Institute, Mumbai, India ; Khaire, Pallavi : Fr. C. Rodrigues Institute of Technology, Navi Mumbai, India ; Phalle, Vikas : Veermata Jijabai Technological Institute, Mumbai, India
Keywordscondition monitoring ; bearing defect ; FFT analyzer ; BPFI ; BPFO ; multiclass support vector machine
Divisions of PASNauki Techniczne
PublisherPolish Academy of Sciences, Committee on Machine Building
Bibliography Z. Taha and N.T. Dung. Rolling element bearing fault detection with a single point defect on the outer raceway using finite element analysis. The 11th Asia Pacific Industrial Engineering and Management Systems Conference and the 14th Asia Pacific Regional Meeting of International Foundation for Production Research, Melaka, Malaysia, 7-10 Dec. 2010.
 P. Jayaswal, S.N. Verma, and A.K. Wadhwani. Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis. Journal of Vibration and Control, 17(8):1131–1148, 2011. doi: 10.1177/1077546310361858.
 V.V. Rao and Ch. Ratnam. A comparative experimental study on identification of defect severity in rolling element bearings using acoustic emission and vibration analysis. Tribology in Industry, 37(2):176–185, 2015.
 S. Shah and A. Guha. Bearing health monitoring. Tribology in Industry, 38(3):297–307, 2016.
 C. Ratnam, N.M. Jasmin, V.V. Rao, and K.V. Rao. A comparative experimental study on fault diagnosis of rolling element bearing using acoustic emission and soft computing techniques. Tribology in Industry, 40(3):501–513, 2018. doi: 10.24874/ti.2018.40.03.15.
 K. Kappaganthu and C. Nataraj. Modelling and analysis of outer race defects in rolling element bearings. Advances in Vibration Engineering, 11(4):371–384, 2012.
 P.K. Kankar, S.C. Sharma, and S.P. Harsha. Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing, 11(2):2300–2312, 2011. doi: 10.1016/j.asoc.2010.08.011.
 A. Sharma, M. Amarnath, and P.K. Kankar. Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control, 22(1):176–192, 2014. doi: 10.1177/1077546314528021.
 V. Hariharan and P.S.S. Srinivasan. Vibration analysis of parallel misaligned shaft with ball bearing system. Sonklanakarin Journal of Science and Technology, 33(1):61–68, 2011.
 J.D. Wu and C.H. Liu. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Systems with Applications, 36(3):4278–4286, 2009. doi: 10.1016/j.eswa.2008.03.008.
 J.S. Rapur and R.Tiwari. Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analyses. Measurement, 147:106809, 2019. doi: 10.1016/j.measurement.2019.07.037.
 C. Cortes and V. Vapnik. Support vector network. Machine Learning, 20(3):273–297, 1995. doi: 10.1007/BF00994018.
 S. Damuluri, K. Islam, P. Ahmadi, and N.S. Qureshi. Analyzing navigational data and predicting student grades using support vector machine. Emerging Science Journal, 4(4):243–252, 2020. doi: 10.28991/esj-2020-01227.
 R. Tiwari. Rotor Systems: Analysis and Identification. CRC Press, 2017. doi: 10.1201/9781315230962.
 V.C. Handikherkar and V.M. Phalle. Gear fault detection using machine learning techniques -- A simulation-driven approach. International Journal of Engineering, 34(1):212–223, 2021. doi: 10.5829/IJE.2021.34.01A.24.
 S. Patil and V. Phalle. Fault detection of anti-friction bearing using ensemble machine learning methods. International Journal of Engineering, 31(11):1972–1981, 2018.
 A.S. Minhas, G. Singh, J. Singh, P.K. Kankarand, and S. Singh. A novel method to classify bearing faults by integrating standard deviation to refined composite multi-scale fuzzy entropy. Measurement,154:107441, 2020. doi: 10.1016/j.measurement.2019.107441.