Abstract
Ball bearings are extensively used in many rotating machinery applications. Ball bearing failure is the main cause of breakdown of the rotating machineries. Local defects in ball bearing produced due to fatigue consist of cracks, pits and spalls on the rolling surfaces. Several researchers have studied the single fault in ball bearing, but in practice two or more faults (combined fault) are usually present. Fault diagnosis of a single row ball bearing has been extensively studied, however literature specific to double row ball bearings are sparse. This paper presents such cases, where single and multiple bearing faults of double row ball bearing are considered together. The objective is to experimentally investigate the single and multiple bearing faults of the inner race, outer race, cage and ball fault using envelope analysis. Envelope spectrum analysis is the best tool for bearing fault diagnosis like cracks and spalls in ball bearings. Bearing frequencies are excellently identified in the envelope spectrum.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Patel VN, Tandon N, Pandey RK (2014) Vibrations generated by rolling element bearings having multiple local defects on races. Procedia Technol 14:312–319
Karacay T, Akturk N (2009) Experimental diagnostics of ball bearings using statistical and spectral methods. Tribol Int 42:836–843
Rafsanjani A, Abbasion S, Farshidianfar A, Moeenfard H (2009) Nonlinear dynamic modeling of surface defects in rolling element bearing systems. J Sound Vib 319:1150–1174
Kiral Z, Karagülle H (2003) Simulation and analysis of vibration signals generated by rolling element bearing with defects. Tribol Int 36:667–678
Choy FK, Wu R, Konrad D, Labus E (2007) Damage identification of ball bearings for transmission systems in household appliances. Tribol Int 50:74–81
Nikolaou NG, Antoniadis IA (2002) Rolling element bearing fault diagnosis using wavelet packets. NDT and E Int 35:197–205
Samanta B, Al-alushi KR (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process 17:317–328
Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning methods. Expert Syst Appl 38:1876–1886
Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnoses of ball bearings using continuous wavelet transform. Appl Soft Comput 11:2300–2312
Kilundu B, Chiementin X, Dehombreux P (2011) Singular spectrum analysis for bearing defect detection. J Vib Acoust 133, 051007-1-051007-7
Wang Y, Liang M (2012) Identification of multiple transient faults based on the adaptive spectral kurtosis method. J Sound Vib 331:470–486
Cong F, Chen J, Dong G, Pecht M (2013) Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis. J Sound Vib 332:2081–2097
Liu WY, Ha JG, Jiang JL (2013) A novel ball bearing fault diagnosis approach based on auto term window method. Measurement 46:4032–4037
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mogal, S.P., Palhe, S.N. (2019). Experimental Investigations of Multiple Faults in Ball Bearing. In: Prasad, A., Gupta, S., Tyagi, R. (eds) Advances in Engineering Design . Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-6469-3_14
Download citation
DOI: https://doi.org/10.1007/978-981-13-6469-3_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6468-6
Online ISBN: 978-981-13-6469-3
eBook Packages: EngineeringEngineering (R0)