The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Diagnosis of stator faults of the single-phase induction motor using acoustic signals
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Integrity signals
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Verification
Claims the paper makes
Citations
Cites
Perceptrons: An Introduction to Computational Geometry
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Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.025 · Cited by 1,710
Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques
Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., Pusca, R., Estima, J., Riera-Guasp, M., & Hedayati-Kia, S. (2014) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2013.2287651 · Cited by 602
A summary of fault modelling and predictive health monitoring of rolling element bearings
El‐Thalji, I., & Jantunen, E. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.02.008 · Cited by 447
Saïdi, L., Ali, J. B., & Fnaiech, F. (2014) · ISA Transactions · doi:10.1016/j.isatra.2014.08.007 · Cited by 206
Caesarendra, W., Kosasih, B., Tieu, A. K., Zhu, H., Moodie, C. A. S., & Zhu, Q. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.020 · Cited by 197
Wang, D. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.007 · Cited by 172
Li, Z., Jiang, Y. S., Hu, C. K., et al. (2016) · Measurement · doi:10.1016/j.measurement.2016.04.036 · Cited by 144
Palácios, R. H. C., da Silva, I. N., Goedtel, A., et al. (2015) · Electric Power Systems Research · doi:10.1016/j.epsr.2015.06.008 · Cited by 131
Głowacz, A., & Głowacz, Z. (2016) · Measurement · doi:10.1016/j.measurement.2016.07.008 · Cited by 103
Research works printed but not linked: 2
- Imandoust, 2013, Application of K-Nearest Neighbor (KNN) approach for predicting economic events: theoretical background, Int J Eng Res Appl, 3, 605
- Arthur D, Vassilvitskii S. K-means++: The advantages of careful seeding. In: SODA ‘07: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms; 2007. p. 1027–35.
Cited by
Sorted by citations
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
Metadata
- Authors
- Glowacz, A., & Glowacz, Z.
- Venue
- Applied Acoustics
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 117
- Pages
- 20-27
- ISSN
- 0003-682X (ISSN-L)
- OpenAlex
- W2543034403
- Cited by
- 124
- References
- 51
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 11 · 2023: 8 · 2022: 8 · 2021: 7 · 2020: 20 · 2019: 15 · 2018: 20 · 2017: 19
- FWCI
- 15.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Acoustics (publishedVersion)
- Funders
- AGH University of Science and Technology (11.11.120.612, 11.11.120.354)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- single-phase induction motor, stator faults, acoustic signal analysis, fault diagnosis, auxiliary winding, feature extraction, k-nearest neighbour classification, K-means clustering, linear perceptron, rotating electrical machines, motor protection
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