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Cited by 263

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Application of an intelligent classification method to mechanical fault diagnosis

Lei, Yaguo, He, Zhengjia, Zi, Yanyang·2009·Expert Systems with Applications
DOI 10.1016/j.eswa.2009.01.065
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Research works printed but not linked: 25

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  3. Chow, 2004, Induction machine fault diagnostic analysis with wavelet technique, IEEE Transactions on Industrial Electronics, 51, 558, 10.1109/TIE.2004.825325 · DOI
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  10. Lehrman, 1997, Symbolic analysis of chaotic signals and turbulent fluctuation, Physical Review Letters, 78, 54, 10.1103/PhysRevLett.78.54 · doi:10.1103/physrevlett.78.54
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  13. Li, 2006, Empirical model decomposition based time-frequency analysis for the effective detection of tool breakage, Transactions of the ASME: Journal of Manufacturing Science and Engineering, 128, 154
  14. Liu, 2006, Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum, Mechanical Systems and Signal Processing, 20, 718, 10.1016/j.ymssp.2005.02.003 · DOI · Paper page
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  16. Ocak, 2007, Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling – A method for bearing prognostics, Journal of Sound and Vibration, 302, 951, 10.1016/j.jsv.2007.01.001 · DOI · Paper page
  17. Peng, 2003, Application of wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography, Mechanical Systems and Signal Processing, 17, 199
  18. Qiao, 2004, Pattern recognition for automatic machinery fault diagnosis, Transactions of the ASME: Journal of Vibration and Acoustic, 126, 307
  19. Samanta, 2004, Artificial neural networks and genetic algorithms for gear fault detection, Mechanical Systems and Signal Processing, 18, 1273, 10.1016/j.ymssp.2003.11.003 · DOI
  20. Sanz, 2007, Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms, Journal of Sound and Vibration, 302, 981, 10.1016/j.jsv.2007.01.006 · DOI
  21. Saxena, 2007, Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems, Applied Soft Computing, 7, 441, 10.1016/j.asoc.2005.10.001 · DOI · Paper page
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Metadata

Authors
Lei, Y., He, Z., & Zi, Y.
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2009-08
Volume
36
Issue
6
Pages
9941-9948
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2009.01.065
OpenAlex
W1965491739
Cited by
263
References
0
FWCI
20.62
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
National Key Research and Development Program of China (2006AA04Z430, 2005CB724106)

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CoChat. (2026). Citation check: Application of an intelligent classification method to mechanical fault diagnosis. https://app.cochat.ai/papers/10.1016/j.eswa.2009.01.065
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Lei, Y., He, Z., & Zi, Y. (2009). Application of an intelligent classification method to mechanical fault diagnosis. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2009.01.065

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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.