The DOI matches its Crossref record.
Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors
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.
Integrity signals
Registry
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.
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: 33
- Back, 1998, A first application of independent components analysis to extracting structure from stock returns, International Journal of Neural System, 8, 473, 10.1142/S0129065797000458 · DOI · Paper page
- Biswall, 1999, Blind source separation of multiple signal sources of MRI data sets using independent components analysis, Journal of Computer Assisted Tomography, 23, 265, 10.1097/00004728-199903000-00016 · DOI · Paper page
- Cao, 2003, A comparison of PCA, KPCA and ICA for dimensional reduction in support vector machine, Neurocomputing, 55, 321, 10.1016/S0925-2312(03)00433-8 · DOI · Paper page
- Cardoso, 1998, Blind signal separation: statistical principles, Proceeding of the IEEE, 86, 2009, 10.1109/5.720250 · DOI · Paper page
- Chan, 2002, Comparison of machine learning and traditional classifiers in glaucoma diagnosis, IEEE Transactions on Biomedical Engineering, 49, 963, 10.1109/TBME.2002.802012 · DOI · Paper page
- Guo, 2003, Research on an integrated ICA-SVM based framework for fault diagnosis, IEEE Journal, 2710
- Han, T., Son, J. D., & Yang, B. S. (2005). Fault diagnosis system of induction motors using feature extraction, feature selection and classification algorithm. Proceedings of VS Tech2005.
- Hsu, 2002, A comparison of methods for multiclass support vector machines, IEEE Transaction on Neural Network, 13, 415, 10.1109/72.991427 · DOI · Paper page
- Hyvärinen, 1998, New approximations of differential entropy for independent component analysis and projection pursuit, Advances in Neural Information Processing System, 10, 273
- Hyvärinen, 1999, Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks, 10, 626, 10.1109/72.761722 · DOI · Paper page
- Hyvärinen, 2000, Independent component analysis: algorithms and applications, Neural Networks, 13, 411, 10.1016/S0893-6080(00)00026-5 · DOI · Paper page
- Jack, 2002, Fault detection using support vector machines and artificial neural network, augmented by genetic algorithms, Mechanical System and Signal Processing, 16, 373, 10.1006/mssp.2001.1454 · DOI · Paper page
- Kriegl, 2005, A support vector machine approach to classify human cytochrome P450 3A4 inhibitors, Journal of Computer-Aided Molecular Design, 19, 189, 10.1007/s10822-005-3785-3 · DOI
- Kumar, 2005, An SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples, Pattern Recognition, 38, 41, 10.1016/j.patcog.2004.06.002 · DOI
- Lee, 1994, An acoustic diagnostic technique for use with electric machine insulation, IEEE Transactions on Dielectrics and Electrical Insulation, 9, 1186, 10.1109/94.368645 · DOI
- Lehrman, 1997, Symbolic analysis of chaotic signals and turbulent fluctuation, Physical Review Letters, 78, 645, 10.1103/PhysRevLett.78.54 · doi:10.1103/physrevlett.78.54
- Min, 2005, Bankruptcy using support vector machine with optimal choice of kernel function parameters, Experts System with Application, 28, 603, 10.1016/j.eswa.2004.12.008 · DOI · Paper page
- Osuna, 1997, Improved training algorithm for support vector machines, Proceeding of IEEE Neural Networks for Signal Processing, 276
- Samanta, 2004, Gear fault detection using artificial neural networks and support vector machines with genetic algorithms, Mechanical Systems and Signal Processing, 18, 625, 10.1016/S0888-3270(03)00020-7 · DOI · Paper page
- Sohn, 2000, Structural health monitoring using statistical process control, Journal of Structural Engineering, 126, 1356, 10.1061/(ASCE)0733-9445(2000)126:11(1356) · doi:10.1061/(asce)0733-9445(2000)126:11(1356 · Paper page
- Stone, 1996, Application of partial discharge testing to motor and generator stator winding maintenance, IEEE Transactions on Industry Application, 32, 459, 10.1109/28.491498 · DOI
- Thollon, 1993, Asynchronous motor cage fault detection through electromagnetic torque measurement, European Transactions on Electrical Power, 3, 375, 10.1002/etep.4450030509 · DOI
- Thomson, 2001, Current signature analysis to detect induction motor faults, IEEE Transactions on Industry Applications, 7, 26
- Vigario, 1997, Extraction of ocular artifacts from EEG using independent components analysis, Electroencephalography and Clinical Neurophysiology, 103, 395, 10.1016/S0013-4694(97)00042-8 · DOI · Paper page
- Worden, 1999, Visualization and dimension reduction of high-dimensional data for damage detection, IMAC, 17, 1576
- Yang, 2004, ART-KOHONEN neural network for faults diagnosis of rotating machinery, Mechanical System and Signal Processing, 18, 645, 10.1016/S0888-3270(03)00073-6 · DOI · Paper page
- Yang, 2005, Fault diagnosis of rotating machinery based on multi-class support vector machines, Journal of Mechanical Science and Technology, 19, 845, 10.1007/BF02916133 · DOI
- Yang, 2005, Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines, Mechanical System and Signal Processing, 19, 371, 10.1016/j.ymssp.2004.06.002 · DOI
- Yang, 2005, Cavitation detection of butterfly valve using support vector machines, Journal of Sound Vibration, 287, 25, 10.1016/j.jsv.2004.10.033 · DOI
- Yang, 2004, Case-based reasoning system with Petri nets for induction motor fault diagnosis, Expert Systems with Applications, 27, 301, 10.1016/j.eswa.2004.02.004 · DOI
- Yang, B. S., Lim, D. S., & An, J. L. (2000). Vibration diagnostic system of rotating machinery using artificial neural network and wavelet transform. Proceeding of 13th International Congress on COMADEM, Houston, USA, pp. 12–20.
- Ypma, A., & Pajunen, A. P. (1999). Rotating machine vibration analysis with second order independent components analysis. Proceeding of the Workshop on ICA and Signal Separation, pp. 37–42.
- Zang, 2004, Structural damage detection using independent components analysis, Structural Health Monitoring, 3, 69, 10.1177/1475921704041876 · DOI
Cited by
Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
Self-citationSupport vector machine in machine condition monitoring and fault diagnosis
Widodo, A., & Yang, B.-S. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.12.007 · Cited by 1,650
Self-citationHigh-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.028 · Cited by 1,162
Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
Widodo, A., Kim, E. Y., Son, J.-D., Yang, B.-S., Tan, A. C. C., Gu, D.-S., Choi, B.-K., & Mathew, J. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.033 · Cited by 324
Self-citationAutomated construction progress measurement using a 4D building information model and 3D data
Kim, C., Son, H., & Kim, C. (2013) · Automation in Construction · doi:10.1016/j.autcon.2012.11.041 · Cited by 317
Metadata
- Authors
- Widodo, A., Yang, B.-S., & Han, T.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2007-02
- Volume
- 32
- Issue
- 2
- Pages
- 299-312
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2031321561
- Cited by
- 296
- References
- 0
- FWCI
- 31.25
- 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: Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. https://app.cochat.ai/papers/10.1016/j.eswa.2005.11.031
- The paper
- Widodo, A., Yang, B.-S., & Han, T. (2007). Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2005.11.031
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.
