Four-Stage Degradation Physics of Rolling Element Bearings
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Rolling element bearings are a critical component of rotating machinery. Timely prediction of bearing faults become of great importance to minimizing unscheduled machine downtime. Most of the bearings experience gradual condition degradation due to repeated mechanical loads.
Vibration signals are often used for bearing diagnosis and prognosis with a predefined threshold. However, false (positive/negative) alarms are often observed, thus leading to unnecessary downtime and expensive corrective maintenance. This is mainly because the thresholds are defined without accounting for bearing physics and a great deal of uncertainty in manufacturing and operation condition. To resolve this difficulty, this study aims at investigating the degradation physics of rolling element bearings using a vibration signal, while accounting for bearing physics and a substantial amount of uncertainty in manufacturing and operation condition. First, bearing feature engineering is thoroughly studied through time
domain and frequency domain analyses. This study proposes the features that are most sensitive to the change in bearing physics. Second, bearing degradation physics is investigated so that the bearing degradation process can be modeled into four degradation stages. To the end, the
proposed idea is demonstrated with vibration data measured from rolling element bearings, which experience accelerated life tested to simulate naturally induced degradation. This study will benefit to enhance physical understanding for bearing faults in various engineering applications.
##plugins.themes.bootstrap3.article.details##
PHM
Yan, W., Qiu, H., & Iyer, N. (2008). Feature extraction for bearing prognostics and health management (phm)-a survey (preprint) (No. AFRL-RX-WP-TP-2008-4309). AIR FORCE RESEARCH LAB WRIGHTPATTERSON AFB OH MATERIALS AND MANUFACTURING DIRECTORATE
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510, doi:10.1016/j.ymssp.2005.09.012
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical systems and signal processing, 42(1), 314-334, doi: 10.1016/j.ymssp.2013.06.004
Sadeghi, F., Jalalahmadi, B., Slack, T. S., Raje, N., & Arakere, N. K. (2009). A review of rolling contact fatigue. Journal of tribology, 131(4), 041403,
doi:10.1115/1.3209132
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), 041101, doi:10.1115/1.4002333
Ahmadi, A. M., Petersen, D., & Howard, C. (2015). A nonlinear dynamic vibration model of defective bearings–The importance of modelling the finite size of rolling elements. Mechanical Systems and Signal Processing, 52, 309-326, doi: 10.1016/j.ymssp.2014.06.006
Hanson, M. T., & Keer, L. M. (1992). An analytical life prediction model for the crack propagation occurring in contact fatigue failure. Tribology transactions, 35(3), 451-461, doi: 10.1080/10402009208982143
El-Thalji, I., & Jantunen, E. (2014). A descriptive model of wear evolution in rolling bearings. Engineering Failure Analysis, 45, 204-224, doi: 10.1016/j.engfailanal.2014.06.004
Dolenc, B., Boškoski, P., & Juričić, Đ. (2016). Distributed bearing fault diagnosis based on vibration analysis. Mechanical Systems and Signal Processing, 66, 521-532, doi: 10.1016/j.ymssp.2015.06.007