The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.
A review on prognostic techniques for non-stationary and non-linear rotating systems
Open accessAbstract
The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,429
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,502
Unscented Filtering and Nonlinear Estimation
Julier, S. J., & Uhlmann, J. K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823141 · Cited by 6,588
An overview of statistical learning theory
Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,266
Gaussian Processes in Machine Learning
Rasmussen, C. E. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-28650-9_4 · Cited by 5,397
A review on machinery diagnostics and prognostics implementing condition-based maintenance
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2005.09.012 · Cited by 4,816
On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,411
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Baum, L. E., & Petrie, T. (1966) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177699147 · Cited by 2,775
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Metadata
- Authors
- Kan, M. S., Tan, A. C. C., & Mathew, J.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 62-63
- Pages
- 1-20
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W1579129365
- Cited by
- 381
- References
- 122
- FWCI
- 29.39
- Type
- journal article
- Language
- en
- Open access
- green
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- Kan, M. S., Tan, A. C. C., & Mathew, J. (2015). A review on prognostic techniques for non-stationary and non-linear rotating systems. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2015.02.016
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