New Approaches of Stochastic Models to Examine the Vibration Features in Roller Bearings
Source abstract
Abstract
Machinery components undergo wear and tear over time due to regular usage, necessitating the establishment of a robust prognosis framework to enhance machinery health and avert catastrophic failures. This study focuses on the collection and analysis of vibration data obtained from roller bearings experiencing various fault conditions. By employing a combination of techniques sourced from existing literature, distinct configurations within vibration datasets were examined to pinpoint the primary defects in roller bearings. The significant features identified through this analysis were utilized to formulate optimized stochastic model equations. These models, developed separately for inner and outer race fault features in comparison to healthy bearing features under random conditions, offer valuable insights into machinery prognosis. The application of these models aids in effective maintenance management, optimization of machinery performance, and the minimization of catastrophic failures and downtime, thereby contributing to overall machinery reliability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Gear and Bearing Dynamics Analysis · Tribology and Lubrication Engineering · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Basic Science and Related Studies, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan
Department of Industrial & Manufacturing Engineering, PN Engineering College, National University of Sciences and Technology (NUST), Karachi 07548, Pakistan
Funding
Mehran University of Engineering and Technology, Jamshoro
References (39)
Show source-provided references
1. Ring Rolling Forming Theory and Technology for Bearing10.3901/JME.2014.16.070 ↗
2. Rolling Bearing Analysis10.1115/1.3617048 ↗
3. Kim, N.-H., An, D., and Choi, J.-H. (2017). Prognostics and Health Management of Engineering Systems, Springer International Publishing.10.1007/978-3-319-44742-1 ↗
4. Ompusunggu, A.P. (2021, January 16–17). Systematic methodology for generating natural spall faults on rolling element bearings. Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021, Oulu, Finland.
5. Fatigue and Fracture Mechanisms in Large Rolling Element Bearings10.1080/10402000108982498 ↗
6. Failure of a set of roller bearings10.1016/0043-1648(57)90270-3 ↗
7. Bearing degradation prognosis using structural break classifier10.5755/j01.mech.4.24.20740 ↗
8. Randall, R.B. (2021). Vibration-Based Condition Monitoring, Wiley. [2nd ed.].10.1002/9781119477631 ↗
9. Are current neural network-based health indicator for remaining useful life prediction of bearings10.1016/j.neucom.2017.02.045 ↗
10. Remaining useful life prediction for ball bearings based on health indicators10.1051/matecconf/201926102003 ↗
11. A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings10.1109/TR.2018.2882682 ↗
12. Remaining useful life prediction of rolling bearings based on Pearson correlation-KPCA multi-feature fusion10.1016/j.measurement.2022.111572 ↗
13. Application of relevance vector machine and survival probability to machine degradation assessment10.1016/j.eswa.2010.08.049 ↗
14. Censoring issues in survival analysis10.1287/opre.50.2.277.426 ↗
15. Gareth, J., Daniela, W., Trevor, H., and Robert, T. (2021). An Introduction to Statistical Learning: With Applications in R, Springer. [2nd ed.].
16. Almutairi, K.M., and Sinha, J.K. (2023). Experimental Vibration Data in Fault Diagnosis: A Machine Learning Approach to Robust Classification of Rotor and Bearing Defects in Rotating Machines. Machines, 11.10.3390/machines11100943 ↗
17. Application of an intelligent classification method to mechanical fault diagnosis10.1016/j.eswa.2009.01.065 ↗
18. Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: Application to the detection of defects in ball bearings10.1016/S0022-460X(03)00483-8 ↗
19. Ovacikli, A.K., Paajarvi, P., LeBlanc, J., and Carlson, J. (2014, January 1–5). Uncovering harmonic content via skewness maximization-a Fourier analysis. Proceedings of the 2014 22nd European Signal Processing Conference (EUSIPCO), IEEE, Lisbon, Portugal.
20. The infogram: Entropic evidence of the signature of repetitive transients10.1016/j.ymssp.2015.04.034 ↗
21. A statistical methodology for the design of condition indicators10.1016/j.ymssp.2018.05.012 ↗
22. Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis
23. Fast computation of the kurtogram for the detection of transient faults10.1016/j.ymssp.2005.12.002 ↗
24. Bishop, C. (2006). Pattern Recognition and Machine Learning, Springer.
25. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model forecology10.1090/S0002-9904-1967-11751-8 ↗
26. Statistical inference for probabilistic functions of finite state Markov chains10.1214/aoms/1177699147 ↗
27. Bearing fault diagnosis using a hidden Markov model based on vibration signals
28. Bearing fault diagnosis based on the hidden Markov model and the support vector machine
29. Bearing fault diagnosis using a Markov chain model and improved ensemble empirical mode decomposition
30. Bearing fault diagnosis based on a hidden Markov model and multiscale permutation entropy
31. Bearing fault diagnosis based on hidden Markov model with hybrid feature extraction
32. A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction10.1109/TIM.2016.2601004 ↗
33. Rolling element bearing diagnostics—A tutorial10.1016/j.ymssp.2010.07.017 ↗
34. Modified Varying Index Coefficient Autoregression Model for Representation of the Nonstationary Vibration from a Planetary Gearbox
35. Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine10.1016/j.ins.2023.119496 ↗
36. A summary of fault modelling and predictive health monitoring of rolling element bearings
37. Liu, C., Xie, L., Wang, D., Zhou, G., Zhou, Q., and Miao, Q. (2014, January 24–27). Application of stochastic resonance in bearing fault diagnosis. Proceedings of the 2014 Prognostics and System Health Management Conference (PHM-2014), Zhangjiajie, China.10.1109/PHM.2014.6988168 ↗
38. Fitting autoregressive model models for prediction10.1007/BF02532251 ↗
39. Estimating the dimension of a model10.1214/aos/1176344136 ↗