Application of Artificial Neural Network in the Investigation of Bearing Defects
Maintenance and design engineers have great concern
for the functioning of rotating machineries due to the vibration
phenomenon. Improper functioning in rotating machinery originates
from the damage to rolling element bearings. The status of rolling
element bearings require advanced technologies to monitor their
health status efficiently and effectively. Avoiding vibration during
machine running conditions is a complicated process. Vibration
simulation should be carried out using suitable sensors/ transducers to
recognize the level of damage on bearing during machine operating
conditions. Various issues arising in rotating systems are interlinked
with bearing faults. This paper presents an approach for fault
diagnosis of bearings using neural networks and time/frequencydomain
vibration analysis.
[1] R. Keith Mobley Boston, “Vibration Fundamentals Part II”,
Butterworth–Heinemann, 1999.
[2] T. Hoshi, “Damage Monitoring of Ball Bearing", Technical Research,
Toyohashi, Japan 2005.
[3] Runqing Huanga, Lifeng Xia, Xinglin Lib, C. Richard Liuc, Hai Qiud,
Jay Lee, “Residual life predictions for ball bearings based on selforganizing
map and back propagation neural network methods’’
[4] Sebastian Willwock and Henning Zoubek Student Member, IEEE,
Mario Pacas Senior Member IEEE, University of Siegen ‘’ Rolling
Bearing Condition Monitoring Based on Frequency Response Analysis’’
Institute of Power Electronics and Electrical Drives Holder 3, IEEE
2007 vol no 4.1.0
[5] M. Subrahmanyam and C. Sujatha “Using neural networks for the
diagnosis of localized defects in ball bearings” Tribology International
Vol. 30, No. 10, pp. 739–752, 1997.
[6] V. Hariharan and PSS. Srinivasan “Vibration Analysis of Shaft-ball
Bearing System” Indian Journal of Science and Technology, Vol.2 No. 9
Sep 2009
[7] H.K.Srinivas, K.S.Srinivasan & K.N.Umesh, Application of artificial
neural network and wavelet transform for vibration analysis of combined
faults of unbalances & shaft bow, Adv.Theor.Appl.Mech, Vol 3, 2010. [8] Menderes Kalkat, Sahin Yildirim & Ibrahim Uzmay, Design of artificial
neural networks for rotor dynamics analysis of rotating machine system,
J Brazil Soci Mech Engrs,15, 573-588,2004 .
[9] M.Senthil Kumar & S.Sendhil Kumar, Effects of misalignment &
Unbalance in a vibration analysis of a rotor-bearing system, National
journal of Technology, Vol 8, No 1,ISSN 0973 1334, 14-20, Mar 2012.
[10] S.Sendhil kumar, M.Senthil Kumar, “Investigation of bearing faults
using LABVIEW”, International journal of Information Technology &
Computer sciences Perspectives” 2219-9016, Vol 1, No1, 208-211, Oct-
Dec’2012
[11] M.Senthil Kumar, S.Sendhil Kumar, “Condition monitoring of rotating
machineries through vibration analysis” Journal of Scientific and
Industrial Research, 0022-4456, Vol 73,258-261, April’2014.
[1] R. Keith Mobley Boston, “Vibration Fundamentals Part II”,
Butterworth–Heinemann, 1999.
[2] T. Hoshi, “Damage Monitoring of Ball Bearing", Technical Research,
Toyohashi, Japan 2005.
[3] Runqing Huanga, Lifeng Xia, Xinglin Lib, C. Richard Liuc, Hai Qiud,
Jay Lee, “Residual life predictions for ball bearings based on selforganizing
map and back propagation neural network methods’’
[4] Sebastian Willwock and Henning Zoubek Student Member, IEEE,
Mario Pacas Senior Member IEEE, University of Siegen ‘’ Rolling
Bearing Condition Monitoring Based on Frequency Response Analysis’’
Institute of Power Electronics and Electrical Drives Holder 3, IEEE
2007 vol no 4.1.0
[5] M. Subrahmanyam and C. Sujatha “Using neural networks for the
diagnosis of localized defects in ball bearings” Tribology International
Vol. 30, No. 10, pp. 739–752, 1997.
[6] V. Hariharan and PSS. Srinivasan “Vibration Analysis of Shaft-ball
Bearing System” Indian Journal of Science and Technology, Vol.2 No. 9
Sep 2009
[7] H.K.Srinivas, K.S.Srinivasan & K.N.Umesh, Application of artificial
neural network and wavelet transform for vibration analysis of combined
faults of unbalances & shaft bow, Adv.Theor.Appl.Mech, Vol 3, 2010. [8] Menderes Kalkat, Sahin Yildirim & Ibrahim Uzmay, Design of artificial
neural networks for rotor dynamics analysis of rotating machine system,
J Brazil Soci Mech Engrs,15, 573-588,2004 .
[9] M.Senthil Kumar & S.Sendhil Kumar, Effects of misalignment &
Unbalance in a vibration analysis of a rotor-bearing system, National
journal of Technology, Vol 8, No 1,ISSN 0973 1334, 14-20, Mar 2012.
[10] S.Sendhil kumar, M.Senthil Kumar, “Investigation of bearing faults
using LABVIEW”, International journal of Information Technology &
Computer sciences Perspectives” 2219-9016, Vol 1, No1, 208-211, Oct-
Dec’2012
[11] M.Senthil Kumar, S.Sendhil Kumar, “Condition monitoring of rotating
machineries through vibration analysis” Journal of Scientific and
Industrial Research, 0022-4456, Vol 73,258-261, April’2014.
@article{"International Journal of Architectural, Civil and Construction Sciences:71733", author = "S. Sendhil Kumar and M. Senthil Kumar", title = "Application of Artificial Neural Network in the Investigation of Bearing Defects", abstract = "Maintenance and design engineers have great concern
for the functioning of rotating machineries due to the vibration
phenomenon. Improper functioning in rotating machinery originates
from the damage to rolling element bearings. The status of rolling
element bearings require advanced technologies to monitor their
health status efficiently and effectively. Avoiding vibration during
machine running conditions is a complicated process. Vibration
simulation should be carried out using suitable sensors/ transducers to
recognize the level of damage on bearing during machine operating
conditions. Various issues arising in rotating systems are interlinked
with bearing faults. This paper presents an approach for fault
diagnosis of bearings using neural networks and time/frequencydomain
vibration analysis.", keywords = "Bearing vibration, Condition monitoring, Fault
diagnosis, Frequency domain.", volume = "9", number = "8", pages = "1121-4", }