The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,564
ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD
WU, Z., & HUANG, N. E. (2009) · Advances in Adaptive Data Analysis · doi:10.1142/s1793536909000047 · Cited by 8,715
Physiological time-series analysis using approximate entropy and sample entropy
Richman, J. S., & Moorman, J. R. (2000) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2000.278.6.h2039 · Cited by 7,880
Approximate entropy as a measure of system complexity.
Pincus, S. M. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.6.2297 · Cited by 5,811
Permutation Entropy: A Natural Complexity Measure for Time Series
Bandt, C., & Pompe, B. (2002) · Physical Review Letters · doi:10.1103/physrevlett.88.174102 · Cited by 4,867
Determining embedding dimension for phase-space reconstruction using a geometrical construction
Kennel, M. B., Brown, R., & Abarbanel, H. D. I. (1992) · Physical Review A · doi:10.1103/physreva.45.3403 · Cited by 3,776
Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review
Nandi, S., Toliyat, H. A., & Li, X. (2005) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2005.847955 · Cited by 2,382
A review on empirical mode decomposition in fault diagnosis of rotating machinery
Lei, Y., Lin, J., He, Z., & Zuo, M. J. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2012.09.015 · Cited by 1,886
Cited by
A new convolutional neural network-based data-driven fault diagnosis method
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2774777 · Cited by 2,222
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2519325 · Cited by 1,191
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey
Zhang, W., Yang, D., & Wang, H. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2905565 · Cited by 732
Ding, Y., Jia, M., Miao, Q., & Cao, Y. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108616 · Cited by 559
Deep residual learning-based fault diagnosis method for rotating machinery
Zhang, W., Li, X., & Ding, Q. (2019) · ISA Transactions · doi:10.1016/j.isatra.2018.12.025 · Cited by 416
Metadata
- Authors
- Zhang, X., Liang, Y., Zhou, J., & zang, Y.
- Venue
- Measurement
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 69
- Pages
- 164-179
- ISSN
- 0263-2241 (ISSN-L)
- OpenAlex
- W1967352108
- Cited by
- 586
- References
- 51
- FWCI
- 43.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. https://app.cochat.ai/papers/10.1016/j.measurement.2015.03.017
- The paper
- Zhang, X., Liang, Y., Zhou, J., & zang, Y. (2015). A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement. https://doi.org/10.1016/j.measurement.2015.03.017
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.
