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Cited by 380

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine

Abbasion, S., Rafsanjani, A., Farshidianfar, A., Irani, N.·2007·Mechanical Systems and Signal Processing
DOI 10.1016/j.ymssp.2007.02.003
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Research works printed but not linked: 29

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  8. Jack, 2001, Support vector machines for detection and characterization of rolling element bearing faults, Proceedings of Institution of Mechanical Engineers, Part C, Journal of Mechanical Engineering Science, 215, 1065, 10.1177/095440620121500907 · DOI
  9. Nikolaou, 2002, Rolling element bearing fault diagnosis using wavelet packets, NDT&E International, 35, 197, 10.1016/S0963-8695(01)00044-5 · DOI · Paper page
  10. Samanta, 2003, Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Engineering Application of Artificial Intelligent, 16, 657, 10.1016/j.engappai.2003.09.006 · DOI · Paper page
  11. Al-Ghamd, 2006, A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size, Mechanical Systems and Signal Processing, 20, 1537, 10.1016/j.ymssp.2004.10.013 · DOI · Paper page
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  23. Lou, 2004, Bearing fault diagnosis based on wavelet transform and fuzzy inference, Journal of Mechanical Systems and Signal Processing, 18, 1077, 10.1016/S0888-3270(03)00077-3 · DOI · Paper page
  24. Li, 2000, Neural network based motor rolling bearing fault diagnosis, IEEE Transaction on Industrial Electronics, 47, 10.1109/41.873214 · DOI · Paper page
  25. Altmann, 2001, Multiple band-pass autoregressive demodulation for rolling element bearing fault diagnosis, Mechanical Systems and Signal Processing, 15, 963, 10.1006/mssp.2001.1410 · DOI
  26. 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
  27. Yang, 2004, ART–KOHONEN neural network for fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, 18, 645, 10.1016/S0888-3270(03)00073-6 · DOI · Paper page
  28. Zhang, 2005, Fault detection using genetic programming, Mechanical Systems and Signal Processing, 19, 271, 10.1016/j.ymssp.2004.03.002 · doi:10.1016/j.ymssp.2004.03.002
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Metadata

Authors
Abbasion, S., Rafsanjani, A., Farshidianfar, A., & Irani, N.
Venue
Mechanical Systems and Signal Processing
Publisher
Elsevier BV
Published
2007-10
Volume
21
Issue
7
Pages
2933-2945
ISSN
0888-3270 (ISSN-L)
DOI
10.1016/j.ymssp.2007.02.003
OpenAlex
W2048522269
Cited by
380
References
1
FWCI
27.97
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: Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. https://app.cochat.ai/papers/10.1016/j.ymssp.2007.02.003
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Abbasion, S., Rafsanjani, A., Farshidianfar, A., & Irani, N. (2007). Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2007.02.003

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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.