Faults Detection of Rolling Element Bearing Due to Misalignment of Rotating Machine
Abstract
Rolling bearing failures are a leading cause of failure in rotating machines, reducing production availability and resulting in costly system downtime. As a result, there is an increasing demand for vibration-based monitoring of bearings, and any method that increases the effectiveness of diagnosing bearing faults should be evaluated. In this paper, we will propose procedures for detecting bearing faults caused by misalignment and data collection for signal processing analysis of all possible operating states of a bearing. The experimental procedures are based on vibration and torque measurements. The test bench's rolling-element bearing is an NTN UCP213-208 pillow block bearing. The experimental work will be conducted using a data acquisition system with a test bench developed by the Department of Machine and Machinery at the Hungarian University of Agricultural and Life Sciences. This work contributes to the development of a predictive method for avoiding rolling element bearing failure.
References
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Gupta, P., & Pradhan, M. K. (2017). Fault detection analysis in rolling element bearing: A review. Materials Today: Proceedings, 4(2), 2085-2094.
Gárdonyi P., Kátai L., Szabó I., (2015). Az ékszíjtárcsa átmérők és az ékszíjak melegedési viszonyainak kapcsolata, Fiatal Műszakiak Tudományos ülésszaka, XX, Kolozsvár, 26-29. o., ISSN 2067-6808
McFadden, P. D., & Smith, J. D. (1984). Model for the vibration produced by a single point defect in a rolling element bearing. Journal of sound and vibration, 96(1), 69-82.
McFadden, P. D., & Smith, J. D. (1985). The vibration produced by multiple point defects in a rolling element bearing. Journal of sound and vibration, 98(2), 263-273.
Tandon, N., & Choudhury, A. (1997). An analytical model for the prediction of the vibration response of rolling element bearings due to a localized defect. Journal of sound and vibration, 205(3), 275-292.
Su, Y. T., & Lin, S. J. (1992). On initial fault detection of a tapered roller bearing: frequency domain analysis. Journal of Sound and Vibration, 155(1), 75-84.
Tandon, N., & Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology international, 32(8), 469-480.
White, M. F. (1984). Simulation and analysis of machinery fault signals. Journal of sound and vibration, 93(1), 95-116.
Patel, V. N., Tandon, N., & Pandey, R. K. (2010). A dynamic model for vibration studies of deep groove ball bearings considering single and multiple defects in races. Journal of Tribology, 132(4).
Patil, M. S., Mathew, J., Rajendrakumar, P. K., & Desai, S. (2010). A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing. International Journal of Mechanical Sciences, 52(9), 1193-1201.
Purohit, R. K., & Purohit, K. (2006). Dynamic analysis of ball bearings with effect of preload and number of balls. International journal of applied mechanics and engineering, 11(1), 77-91.
Harsha, S. P., Sandeep, K., & Prakash, R. (2004). Non-linear dynamic behaviors of rolling element bearings due to surface waviness. Journal of sound and vibration, 272(3-5), 557-580.
Kiral, Z., & Karagülle, H. (2003). Simulation and analysis of vibration signals generated by rolling element bearing with defects. Tribology International, 36(9), 667-678.
Shah, D. S., & Patel, V. N. (2014). A review of dynamic modeling and fault identifications methods for rolling element bearing. Procedia Technology, 14, 447-456.
YILMAZ, Ö., Aksoy, M., & Kesilmiş, Z. (2019). Misalignment fault detection by wavelet analysis of vibration signals. International Advanced Researches and Engineering Journal, 3(3), 156-163.
Prytz, R. (2014). Machine learning methods for vehicle predictive maintenance using off-board and on-board data (Doctoral dissertation, Halmstad University Press).
Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150-172.
Kothamasu, R., Huang, S. H., & VerDuin, W. H. (2006). System health monitoring and prognostics—a review of current paradigms and practices. The International Journal of Advanced Manufacturing Technology, 28(9-10), 1012-1024
Myers, L.J., Erim, Z. & Lowery, M.M (2004). Time and frequency domain methods for quantifying common modulation of motor unit firing patterns. J NeuroEngineering Rehabil 1, 2. https://doi.org/10.1186/1743-0003-1-2
Gupta, P., & Pradhan, M. K. (2017). Fault detection analysis in rolling element bearing: A review. Materials Today: Proceedings, 4(2), 2085-2094.
Gárdonyi P., Kátai L., Szabó I., (2015). Az ékszíjtárcsa átmérők és az ékszíjak melegedési viszonyainak kapcsolata, Fiatal Műszakiak Tudományos ülésszaka, XX, Kolozsvár, 26-29. o., ISSN 2067-6808
McFadden, P. D., & Smith, J. D. (1984). Model for the vibration produced by a single point defect in a rolling element bearing. Journal of sound and vibration, 96(1), 69-82.
McFadden, P. D., & Smith, J. D. (1985). The vibration produced by multiple point defects in a rolling element bearing. Journal of sound and vibration, 98(2), 263-273.
Tandon, N., & Choudhury, A. (1997). An analytical model for the prediction of the vibration response of rolling element bearings due to a localized defect. Journal of sound and vibration, 205(3), 275-292.
Su, Y. T., & Lin, S. J. (1992). On initial fault detection of a tapered roller bearing: frequency domain analysis. Journal of Sound and Vibration, 155(1), 75-84.
Tandon, N., & Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology international, 32(8), 469-480.
White, M. F. (1984). Simulation and analysis of machinery fault signals. Journal of sound and vibration, 93(1), 95-116.
Patel, V. N., Tandon, N., & Pandey, R. K. (2010). A dynamic model for vibration studies of deep groove ball bearings considering single and multiple defects in races. Journal of Tribology, 132(4).
Patil, M. S., Mathew, J., Rajendrakumar, P. K., & Desai, S. (2010). A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing. International Journal of Mechanical Sciences, 52(9), 1193-1201.
Purohit, R. K., & Purohit, K. (2006). Dynamic analysis of ball bearings with effect of preload and number of balls. International journal of applied mechanics and engineering, 11(1), 77-91.
Harsha, S. P., Sandeep, K., & Prakash, R. (2004). Non-linear dynamic behaviors of rolling element bearings due to surface waviness. Journal of sound and vibration, 272(3-5), 557-580.
Kiral, Z., & Karagülle, H. (2003). Simulation and analysis of vibration signals generated by rolling element bearing with defects. Tribology International, 36(9), 667-678.
Shah, D. S., & Patel, V. N. (2014). A review of dynamic modeling and fault identifications methods for rolling element bearing. Procedia Technology, 14, 447-456.
YILMAZ, Ö., Aksoy, M., & Kesilmiş, Z. (2019). Misalignment fault detection by wavelet analysis of vibration signals. International Advanced Researches and Engineering Journal, 3(3), 156-163.
Prytz, R. (2014). Machine learning methods for vehicle predictive maintenance using off-board and on-board data (Doctoral dissertation, Halmstad University Press).
Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150-172.
Kothamasu, R., Huang, S. H., & VerDuin, W. H. (2006). System health monitoring and prognostics—a review of current paradigms and practices. The International Journal of Advanced Manufacturing Technology, 28(9-10), 1012-1024
Published
2021-06-01
How to Cite
ALBDERY, Mohsin Hassan; SZABÓ, István.
Faults Detection of Rolling Element Bearing Due to Misalignment of Rotating Machine.
International Journal of Advanced Research in Engineering Innovation, [S.l.], v. 3, n. 2, p. 14-22, june 2021.
Available at: <https://myjms.mohe.gov.my/index.php/ijarei/article/view/13434>. Date accessed: 11 sep. 2024.
Section
Articles
