Details

Title

A smart fault identification system for ball bearing using simulation-driven vibration analysis

Journal title

Archive of Mechanical Engineering

Yearbook

2023

Volume

vol. 70

Issue

No 2

Affiliation

Khaire, 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

Authors

Keywords

condition monitoring ; bearing defect ; FFT analyzer ; BPFI ; BPFO ; multiclass support vector machine

Divisions of PAS

Nauki Techniczne

Coverage

247-270

Publisher

Polish Academy of Sciences, Committee on Machine Building

Bibliography

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[2] 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.
[3] 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.
[4] S. Shah and A. Guha. Bearing health monitoring. Tribology in Industry, 38(3):297–307, 2016.
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[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
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[14] R. Tiwari. Rotor Systems: Analysis and Identification. CRC Press, 2017. doi: 10.1201/9781315230962.
[15] 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.
[16] 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.
[17] 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.
[18] www.mfpt.org

Date

19.06.2023

Type

Article

Identifier

DOI: 10.24425/ame.2023.145583
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