PRECISION IN PERIL: A HOLISTIC APPROACH TO DETERMINING FAILURE THRESHOLDS FOR ROLLING ELEMENT BEARINGS
Keywords:
Rolling element bearings, Failure thresholds, Predictive maintenanceAbstract
The reliable operation of rolling element bearings is crucial in various industrial applications, yet predicting their failure remains challenging. This paper presents a comprehensive approach for determining failure thresholds in rolling element bearings. By integrating experimental testing, statistical analysis, and predictive modeling, this approach aims to identify key indicators and parameters that precede bearing failure. Through accelerated life testing and condition monitoring, data on bearing performance, vibration, temperature, and lubricant condition are collected and analyzed. Statistical methods such as Weibull analysis and survival analysis are applied to quantify the probability of failure and estimate the remaining useful life of bearings. Additionally, machine learning techniques are employed to develop predictive models that correlate operational parameters with bearing degradation and failure. By combining these approaches, a holistic understanding of bearing failure mechanisms is achieved, enabling proactive maintenance strategies and improved reliability in industrial systems.
Downloads
References
Mahamad, A.K.; Saon, S.; Hiyama, T. Predicting remaining useful life of rotating machinery based artificial neural network. Comput. Math. Appl. 2010, 60, 1078–1087.
ISO. Standard ISO 13381-1. Condition Monitoring and Diagnostics of Machines—Prognostics—Part 1: General Guidelines; ISO: Geneva, Switzerland, 2015.
ISO. Standard ISO 10816-3. Mechanical Vibration—Evaluation of Machine Vibration by Measurements on Non-Rotating Parts—Part 3: Industrial Machines with Normal Power above 15 kW and Nominal Speeds between 120 r/min and 15000 r/min; ISO: Geneva, Switzerland, 2009.
Elasha, F.; Shanbr, S.; Li, X.; Mba, D. Prognosis of a wind turbine gearbox bearing using supervised machine learning. Sensors 2019, 19, 3092.
Wang, D.; Tsui, K.L. Two novel mixed effects models for prognostics of rolling element bearings. Mech. Syst. Signal Process. 2018, 99, 1–13.
Wang, Y.; Peng, Y.; Zi, Y.; Jin, X.; Tsui, K.L. A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem. IEEE Trans. Ind. Inform. 2016, 12, 924–932.
Lim, C.K.R.; Mba, D. Switching Kalman filter for failure prognostic. Mech. Syst. Signal Process. 2015, 52, 426–435.
Bastami, A.R.; Aasi, A.; Arghand, H.A. Estimation of remaining useful life of rolling element bearings using wavelet packet decomposition and artificial neural network. IJSTE 2019, 43, 233–245.
Nectoux, P.; Gouriveau, R.; Medjaher, K.; Ramasso, E.; Chebel-Morello, B.; Zerhouni, N.; Varnier, C. PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In Proceedings of the IEEE International Conference on Prognostics and Health Management, Denver, CO, USA, 18–21 June 2012; Available online: https://hal.archives-ouvertes.fr/hal-00719503 (accessed on 20 July 2012).
Behzad, M.; Arghand, H.A.; Bastami, A.R. Remaining useful life prediction of ball-bearings based on high-frequency vibration features. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2018, 232, 3224–3234.
Behzad, M.; Arghand, H.A.; Bastami, A.R. Rolling Element Bearings Prognostics Using High-Frequency Spectrum of Offline Vibration Condition Monitoring Data. In Proceedings of the Condition Monitoring and Diagnostic Engineering Management, Rustenburg, South Africa, 2–5 July 2018.
Li, N.; Lei, Y.; Lin, J.; Ding, S.X. An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Trans. Ind. Electron. 2015, 62, 7762–7773.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Sajjad Hassan
This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in the Journal of Applied Science and Social Science (JASSS) is protected by copyright. Authors retain the copyright to their work, and grant JASSS the right to publish the work under a Creative Commons Attribution License (CC BY). This license allows others to distribute, remix, adapt, and build upon the work, even commercially, as long as they credit the author(s) for the original creation.
