The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem
Abstract
Prognostics of the remaining useful life (RUL) has emerged as a critical technique for ensuring the safety, availability, and efficiency of a complex system. To gain a better prognostic result, degradation information is quite useful because it can reflect the health status of a system. However, due to the lack of accurate information about the plants' degradation, the prognostic model is usually not well established. To solve this problem, this paper proposes a two-stage strategy that is in the context of data-driven modeling to predict the future health status of a bearing, where the degradation information was estimated by calculating the deviation of multiple statistics of vibration signals of a bearing from a known healthy state. Then, a prediction stage based on an enhanced Kalman filter and an expectation-maximization algorithm were used to estimate the RUL of the bearing adaptively. To verify the effectiveness of the proposed approach, a real-bearing degradation problem was implemented.
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Cites
Remaining useful life estimation – A review on the statistical data driven approaches
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.11.018 · Cited by 2,162
Maximum likelihood estimates of linear dynamic systems
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A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
Yin, S., Ding, S. X., Xie, X., & Luo, H. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2301773 · Cited by 1,716
He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
Prognostics and Health Management of Electronics
Pecht, M. G. (2008) · Wiley eBooks · doi:10.1002/9780470385845 · Cited by 847
Residual-life distributions from component degradation signals: A Bayesian approach
GEBRAEEL, N. Z., LAWLEY, M. A., LI, R., & RYAN, J. K. (2005) · IIE Transactions · doi:10.1080/07408170590929018 · Cited by 788
Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process
Si, X.-S., Wang, W., Hu, C.-H., Zhou, D.-H., & Pecht, M. G. (2012) · IEEE Transactions on Reliability · doi:10.1109/tr.2011.2182221 · Cited by 662
Modelling Accelerated Degradation Data Using Wiener Diffusion With A Time Scale Transformation
Whitmore, G. A., & Schenkelberg, F. (1997) · Lifetime Data Analysis · doi:10.1023/a:1009664101413 · Cited by 489
Si, X.-S., Wang, W., Hu, C.-H., Chen, M.-Y., & Zhou, D.-H. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2012.08.016 · Cited by 457
Prognostics and health management of electronics
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Cited by
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Wang, B., Lei, Y., Li, N., & Li, N. (2020) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2882682 · Cited by 1,917
Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators
Wang, D., Tsui, K.-L., & Miao, Q. (2018) · IEEE Access · doi:10.1109/access.2017.2774261 · Cited by 455
Self-citationRUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model
Zhai, Q., & Ye, Z.-S. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2684821 · Cited by 363
Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis
Jin, X., Xu, Z., & Qiao, W. (2021) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2020.2989220 · Cited by 209
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Metadata
- Authors
- Wang, Y., Peng, Y., Zi, Y., Jin, X., & Tsui, K.-L.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2016-06
- Volume
- 12
- Issue
- 3
- Pages
- 924-932
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2319025975
- Cited by
- 344
- References
- 32
- FWCI
- 26.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, Y., Peng, Y., Zi, Y., Jin, X., & Tsui, K.-L. (2016). A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2016.2535368
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