Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 518

The DOI matches its Crossref record.

Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection

Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A.·2003·Engineering Applications of Artificial Intelligence
DOI 10.1016/j.engappai.2003.09.006
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.engappai.2003.09.006

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 24

  1. Al-Balushi, 2002, Gear fault diagnosis using energy-based features of acoustic emission signals, Proceedings of IMechE, Part I: Journal of Systems and Control Engineering, 216, 249, 10.1243/095965102320005418 · doi:10.1243/095965102320005418
  2. 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
  3. 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
  4. Campbell, C., 2000. Algorithmic approaches to training support vector machines: a survey. Proceedings of ESANN2000 Bruges, Belgium, Paper #ESANN2000-355.
  5. Dellomo, 1999, Helicopter gearbox fault detection, Transactions of the ASME, Journal of Vibration and Acoustics, 121, 265, 10.1115/1.2893975 · DOI
  6. Guyon, I., Christianini, N., 1999. Survey of support vector machine applications. http://www.clopinet.com/isabelle/Projects/SVM/applist.html, Proceedings of NIPS’99 Special Workshop on Learning with Support Vector.
  7. Jack, L.B., Nandi, A.K., 2000a. Comparison of neural networks and support vector machines in condition monitoring applications. Proceedings of COMADEM 2000, Houston, TX, USA, pp. 721–730.
  8. 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
  9. Jack, 2001, 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
  10. 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
  11. McCormick, 1997, Classification of the rotating machine condition using artificial neural networks, Proceedings of IMechE, Part C: Journal of Mechanical Engineering Science, 211, 439, 10.1243/0954406971521845 · DOI
  12. 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
  13. Michalewicz, Z., 1999. Genetic Algorithms+Data Structures=Evolution Programs, third ed. Springer, New York, USA.
  14. Nandi, A.K., 2000. Advanced digital vibration signal processing for condition monitoring. Proceedings of COMADEM 2000, Houston, TX, USA, pp. 129–143.
  15. Platt, 1998, How to implement SVMs, IEEE Intelligent Systems, 13, 26
  16. Randall, R.B. (guest ed.), 2001. Special issue on gear and bearing diagnostics. Mechanical Systems and Signal Processing 15(5), 827–993. · doi:10.1006/mssp.2001.1419
  17. Samanta, 2001, Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system, Proceedings of IMechE, Part I: Journal of Systems and Control Engineering, 215, 199, 10.1243/0959651011540987 · DOI
  18. Samanta, 2003, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mechanical Systems and Signal Processing, 17, 317, 10.1006/mssp.2001.1462 · DOI · Paper page
  19. Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A., 2001. Use of genetic algorithm and artificial neural network for gear condition diagnostics. Proceedings of COMADEM 2001, University of Manchester, UK, pp. 449–456. · DOI
  20. Scholkopf, 1998, SVMs—a practical consequence of learning theory, IEEE Intelligent Systems, 13, 18
  21. 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
  22. Tang, 1996, Genetic algorithms and their applications, IEEE Signal Processing Magazine, 13, 22, 10.1109/79.543973 · DOI · Paper page
  23. Vapnik, 1999, An overview of statistical learning theory, IEEE transactions on Neural Networks, 10, 988, 10.1109/72.788640 · DOI · Paper page
  24. Weston, J., Watkins, C., 1999. Support vector machines for multi-class recognition. Proceedings of ESANN’99, Bruges, Belgium, pp. 219–224.

Cited by

See all 518

Metadata

Authors
Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A.
Venue
Engineering Applications of Artificial Intelligence
Publisher
Elsevier BV
Published
2003-10
Volume
16
Issue
7-8
Pages
657-665
ISSN
0952-1976 (ISSN-L)
DOI
10.1016/j.engappai.2003.09.006
OpenAlex
W2063789075
Cited by
518
References
0
FWCI
17.20
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. https://app.cochat.ai/papers/10.1016/j.engappai.2003.09.006
The paper
Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2003). Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2003.09.006

Data sources

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.