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Cited by 517

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Gear fault detection using artificial neural networks and support vector machines with genetic algorithms

Samanta, B.·2004·Mechanical Systems and Signal Processing
DOI 10.1016/s0888-3270(03)00020-7
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Research works printed but not linked: 23

  1. Shiroishi, 1997, Bearing condition diagnostics via vibration and acoustic emission measurements, Mechanical Systems and Signal Processing, 11, 693, 10.1006/mssp.1997.0113 · DOI · Paper page
  2. McFadden, 2000, Detection of gear faults by decomposition of matched differences of vibration signals, Mechanical Systems and Signal Processing, 14, 805, 10.1006/mssp.2000.1318 · DOI
  3. A.K. Nandi, Advanced digital vibration signal processing for condition monitoring, Proceedings of 13th Conference of Condition Monitoring and Diagnostic Engineering Management (COMADEM), Houston, TX, USA, 2000, pp. 129–143.
  4. 2001, Special issue on gear and bearing diagnostics, Mechanical Systems and Signal Processing, 15, 317
  5. Al-Balushi, 2002, Gear fault diagnosis using energy-based features of acoustic emission signals, Proceedings of Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 216, 249, 10.1243/095965102320005418 · doi:10.1243/095965102320005418
  6. Antoni, 2002, Differential diagnosis of gear and bearing faults, Transactions of the ASME, Journal of Vibration and Acoustics, 124, 165, 10.1115/1.1456906 · DOI · Paper page
  7. McCormick, 1997, Classification of the rotating machine condition using artificial neural networks, Proceedings of Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 211, 439, 10.1243/0954406971521845 · DOI
  8. Dellomo, 1999, Helicopter gearbox fault detection, Transactions of the ASME, Journal of Vibration and Acoustics, 121, 265, 10.1115/1.2893975 · DOI
  9. Samanta, 2001, Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system, Proceedings of Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 215, 199, 10.1243/0959651011540987 · DOI
  10. B. Samanta, K.R. Al-Balushi, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mechanical Systems and Signal Processing, 2002, to appear. · DOI · Paper page
  11. Burges, 1998, A tutorial on support vector machines for pattern recognition, Data mining and knowledge discovery, 2, 955, 10.1023/A:1009715923555 · DOI · Paper page
  12. I. Guyon, N. Christianini, Survey of support vector machine applications, Proceedings of NIPS’99 Special Workshop on Learning with Support Vector, 1999, http://www.clopinet.com/isabelle/Projects/SVM/applist.html
  13. Scholkopf, 1998, SVMs—a practical consequence of learning theory, IEEE Intelligent Systems, 13, 18
  14. Vapnik, 1999, An overview of statistical learning theory, IEEE Transactions on Neural Networks, 10, 988, 10.1109/72.788640 · DOI · Paper page
  15. L.B. Jack, A.K. Nandi, Comparison of neural networks and support vector machines in condition monitoring applications, Proceedings of 13th Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM), Houston, TX, USA, 2000, pp. 721–730.
  16. Jack, 2000, Support vector machines for detection and characterisation of rolling element bearing faults, Proceedings of Institution of Mechanical Engineers. Part C: Journal of Mechanical Engineering Science, 215, 1065, 10.1177/095440620121500907 · DOI
  17. Jack, 2000, Genetic algorithms for feature extraction in machine condition monitoring with vibration signals, IEE Proceedings of Vision & Image Signal Processing, 147, 205, 10.1049/ip-vis:20000325 · DOI
  18. Jack, 2002, Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms, Mechanical Systems and Signal Processing, 16, 373, 10.1006/mssp.2001.1454 · DOI · Paper page
  19. B. Samanta, K.R. Al-Balushi, S.A. Al-Araimi, Use of genetic algorithm and artificial neural network for gear condition diagnostics, Proceedings of COMADEM 2001, University of Manchester, UK, 2001, pp. 449–456. · DOI
  20. J. Weston, C. Watkins, Support vector machines for multi-class recognition, Proceedings of ESANN’99, Burges, Belgium, 1999, pp. 219–224.
  21. Platt, 1998, How to implement SVMs, IEEE Intelligent Systems, 13, 26
  22. C. Campbell, Algorithmic approaches to training support vector machines: a survey, Proceedings of ESANN2000 Burges, Belgium, Paper #ESANN2000-355, 2000.
  23. Tang, 1996, Genetic algorithms and their applications, IEEE Signal Processing Magazine, 13, 22, 10.1109/79.543973 · DOI · Paper page

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Metadata

Authors
Samanta, B.
Venue
Mechanical Systems and Signal Processing
Publisher
Elsevier BV
Published
2004-05
Volume
18
Issue
3
Pages
625-644
ISSN
0888-3270 (ISSN-L)
DOI
10.1016/s0888-3270(03)00020-7
OpenAlex
W2074724805
Cited by
517
References
0
FWCI
20.65
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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CoChat. (2026). Citation check: Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. https://app.cochat.ai/papers/10.1016/s0888-3270(03)00020-7
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Samanta, B. (2004). Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing. https://doi.org/10.1016/s0888-3270(03)00020-7

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.