The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS
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
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
Vapnik, V. N., & Chervonenkis, A. Y. (1971) · Theory of Probability & Its Applications · doi:10.1137/1116025 · Cited by 2,448
Research works printed but not linked: 21
- MCCORMICK, 1997, Classification of rotating machine condition using artificial neural networks, Proceedings of the I-MechE: Part C, 11, 439
- BAILLIE, 1996, A Comparison of autoregressive modelling techniques for fault diagnosis of rolling element bearing, Mechanical systems and Signal Processing, 10, 1, 10.1006/mssp.1996.0001 · DOI
- SORSA, 1991, Neural networks in process fault diagnosis, IEEE Transactions on Systems, Man, and Cybernetics, 21, 815, 10.1109/21.108299 · DOI · Paper page
- POLYCARPOU, 1995, Automated fault detection and accommodation: a learning systems approach, IEEE Transactions on Systems, Man, and Cybernetics, 25, 1447, 10.1109/21.467710 · DOI
- LIU, 1992, Intelligent monitoring of ball bearing conditions, Mechanical Systems and Signal Processing, 6, 419, 10.1016/0888-3270(92)90066-R · DOI
- R. FERNÁNDEZ, E. VIENNET, 1999, Proceedings of ESANN’99, 195, 200, Face identification using support vector machines
- 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
- I. GUYON, N. CHRISTIANINI, 1999, Proceedings of NIPS'99 Special Workshop on Learning with Support Vectors, Survey of support vector machine applications, http://www.clopinet.com/isabelle/Projects/SVM/applist.html
- JACK, 2000, Support vector machines for detection and characterisation of rolling element bearing faults, Proceedings of the Institution of Mechanical Engineers, Part C, 215, 1065
- J. YANG, V. HONAVAR, 1997, Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, 13, 16, 1, 24, Los Altos, CA, Morgan Kaufmann, J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, R. L. Riolo, Feature subset selection using A genetic algorithm
- JAIN, 1997, Feature selection: evaluation, application and small sample performance, IEEE Transactions on Pattern Matching and Machine Intelligence, 19, 153, 10.1109/34.574797 · DOI · Paper page
- B. DOM, W. NIBLACK, J. SHEINVALD, 1989, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 241, 248, Feature selection with stochastic complexity · doi:10.1109/cvpr.1989.37856
- JACK, 2000, Genetic algorithms for feature selection in machine condition monitoring with vibration signals, IEE Proceedings—Vision, Image and Signal Processing, 147, 205, 10.1049/ip-vis:20000325 · DOI
- MCCORMICK, 1998, Digital signal processing algorithms in condition monitoring, International Journal of COMADEM, 1, 5
- MCCORMICK, 1997, Real time classification of rotating shaft loading conditions using artificial neural networks, IEEE Transactions on Neural Networks, 8, 748, 10.1109/72.572110 · DOI
- J. WESTON, C. WATKINS, 1999, Proceedings of ESANN'99, Bruges, Belgium, 219, 224, Support vector machines for multi-class pattern recognition
- SCHÖLKOPF, 1998, SVMs — a practical consequence of learning theory, IEEE Intelligent Systems, 13, 18
- TANG, 1996, Genetic algorithms and their applications, IEEE Signal Processing Magazine, 13, 22, 10.1109/79.543973 · DOI · Paper page
- WHITELY, 1994, A genetic algorithms tutorial, Statistics and Computing, 4, 65
- KUDO, 2000, Comparison of algorithms that select features for pattern classifiers, Pattern Recognition, 33, 25, 10.1016/S0031-3203(99)00041-2 · DOI
- MANGASARIAN, 1999, Successive overrelaxation for support vector machines, IEEE Transactions on Neural networks, 10, 1032, 10.1109/72.788643 · DOI · Paper page
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
Fault diagnosis of rotating machinery based on multiple ANFIS combination with gas
Lei, Y., He, Z., Zi, Y., & Hu, Q. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.11.003 · Cited by 500
Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach
He, D., Li, R., & Zhu, J. (2012) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2192894 · Cited by 143
Gangsar, P., & Tiwari, R. (2014) · Journal of Failure Analysis and Prevention · doi:10.1007/s11668-014-9893-4 · Cited by 25
Metadata
- Authors
- JACK, L. B., & NANDI, A. K.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2002-03
- Volume
- 16
- Issue
- 2-3
- Pages
- 373-390
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W1995352662
- Cited by
- 376
- References
- 1
- FWCI
- 13.69
- 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: FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS. https://app.cochat.ai/papers/10.1006/mssp.2001.1454
- The paper
- JACK, L. B., & NANDI, A. K. (2002). FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS. Mechanical Systems and Signal Processing. https://doi.org/10.1006/mssp.2001.1454
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.
