Reliability Analysis of Rolling Bearings Using a Weighted Nonlinear Mixed-Effects Degradation Model
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Abstract
Reliability assessment of rolling element bearings is critical for the predictive maintenance of industrial rotary machinery.
This study proposes a Quadratic-Exponential Weighted Model (QEWM) based on Nonlinear Mixed-Effects (NLME) to characterize the degradation process of bearings. Utilizing the IMS Bearing Dataset (Set No. 2), we define the failure threshold based on the latest ISO 20816-3:2022 vibration severity standards, setting the critical RMS limit at 0.4 mm/s for Zone D. Unlike traditional models, the proposed QEWM incorporates a weight function to address heteroscedasticity, which typically intensifies during the rapid degradation phase. Model comparison based on the Akaike Information Criterion (AIC)
demonstrates that QEWM significantly outperforms linear and unweighted quadratic models. To quantify the uncertainty of
the estimation, a parametric bootstrap method with 5,000 replications was employed. The results identify a B10 life (t0.1) of
165.3 hours, supported by a precise 95% confidence interval of [162.7, 168.6] hours. This research provides a robust statistical framework for bearing life prediction that aligns with international industrial standards, ensuring high precision in
prognostic assessments.
How to Cite
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Reliability, Rolling Bearings, Nonlinear Mixed-Effects Model, Degradation Analysis
Davidian, M., & Giltinan, D. M. (1995). Nonlinear models for repeated measurement data. New York: Chapman & Hall.
Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. New York: CRC Press.
Gebraeel, N., Lawley, M., Li, R., & Ryan, J. K. (2005). Residual-life distributions from component degradation signals: A Bayesian approach. IIE Transactions, 37(6), 543–557. doi: 10.1080/07408170590929018
IMS Center. (2004). IMS bearing dataset. https://www.imscenter.net. NSF I/UCR Center for Intelligent Maintenance Systems.
International Organization for Standardization. (2022). Mechanical vibration—measurement and evaluation of machine vibration—Part 3: Industrial machines with nominal power above 15 kW and nominal speeds between 120 r/min and 30 000 r/min when measured in situ (ISO 20816-3:2022). https://www.iso.org/standard/78189.html. Geneva, Switzerland.
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(1–2), 314–334. doi: 10.1016/j.ymssp.2013.06.004
Lin, J., Liao, G., Chen, M., & Yin, H. (2021). Two-phase degradation modeling and remaining useful life prediction using nonlinear Wiener process. Computers & Industrial Engineering, 160, 107533. doi: 10.1016/j.cie.2021.107533
Lindstrom, M. J., & Bates, D. M. (1990). Nonlinear mixed effects models for repeated measures data. Biometrics, 46(3), 673–687.
Meeker, W. Q., Escobar, L. A., & Pascual, F. G. (2021). Statistical methods for reliability data (2nd ed.). Wiley.
Meng, Z., Li, J., Yin, N., & Pan, Z. (2020). Remaining useful life prediction of rolling bearing using fractal theory. Measurement, 161, 107572. doi: 10.1016/j.measurement.2020.107572
Rubinstein, R. Y., & Kroese, D. P. (2017). Simulation and the Monte Carlo method (3rd ed.). Wiley.
Wang, L., & Gao, R. X. (2006). Condition monitoring and control for intelligent manufacturing. London: Springer.

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