The DOI matches its Crossref record.
Vibration and current monitoring for detecting airgap eccentricity in large induction motors
Abstract
The paper reports on a study carried out to detect airgap eccentricity in large 3-phase induction motors. The philosophy of using a unified online monitoring strategy is presented and the reasons for selecting line current and frame vibration as the monitored parameters are discussed. A theoretical analysis is presented which predicts the presence of unique signature patterns in the current and vibration spectra which are only characteristic of eccentricity. The theoretical predictions are verified by experimental results from a special fault producing test rig and on-site tests in a power station.
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
Cited by
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
Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition
Cusid�Cusido, J., Romeral, L., Ortega, J. A., Rosero, J. A., & Garc�aGarcia Espinosa, A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2007.911960 · Cited by 512
Rajagopalan, S., Roux, W. L., Habetler, T. G., & Harley, R. G. (2007) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2007.904183 · Cited by 164
Park, Y., Fernandez, D., Lee, S. B., Hyun, D., Jeong, M., Kommuri, S. K., Cho, C., Diaz Reigosa, D., & Briz, F. (2019) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2018.2886772 · Cited by 116
Climente-Alarcon, V., Antonino-Daviu, J. A., Riera-Guasp, M., Puche-Panadero, R., & Escobar, L. (2012) · Electric Power Systems Research · doi:10.1016/j.epsr.2012.05.001 · Cited by 78
Metadata
- Authors
- Cameron, J. R., Thomson, W. T., & Dow, A. B.
- Venue
- IEE Proceedings B Electric Power Applications
- Published
- 1986
- Volume
- 133
- Issue
- 3
- Pages
- 155
- ISSN
- 0143-7038 (ISSN-L)
- OpenAlex
- W2138461713
- Cited by
- 512
- References
- 0
- FWCI
- 4.09
- Type
- journal article
- Language
- en
- Open access
- closed
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: Vibration and current monitoring for detecting airgap eccentricity in large induction motors. https://app.cochat.ai/papers/10.1049/ip-b.1986.0022
- The paper
- Cameron, J. R., Thomson, W. T., & Dow, A. B. (1986). Vibration and current monitoring for detecting airgap eccentricity in large induction motors. IEE Proceedings B Electric Power Applications. https://doi.org/10.1049/ip-b.1986.0022
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.