Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 301

The DOI matches its Crossref record.

A Survey of Methods for Detection of Stator-Related Faults in Induction Machines

Tallam, Rangarajan M., Lee, Sang Bin, Stone, Greg C., Kliman, Gerald B., Yoo, Jiyoon, Habetler, Thomas G., Harley, Ronald G.·2007·IEEE Transactions on Industry Applications

Abstract

As evidenced by industrial surveys, stator-related failures account for a large percentage of faults in induction machines. The objective of this paper is to provide a survey of existing techniques for detection of stator-related faults, which include stator winding turn faults, stator core faults, temperature monitoring and thermal protection, and stator winding insulation testing. The root causes of fault inception, available techniques for detection, and recommendations for further research are presented. Although the primary focus is online and sensorless methods that use machine voltages and currents to extract fault signatures, offline techniques such as partial discharge detection are also examined. Condition monitoring, fault diagnostics, insulation testing, interlaminar core faults, partial discharge (PD), temperature monitoring, turn faults.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tia.2007.900448

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.

A few free checks a day · papers already verified open instantly · results are public

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

See all 301

Metadata

Authors
Tallam, R. M., Lee, S. B., Stone, G. C., Kliman, G. B., Yoo, J., Habetler, T. G., & Harley, R. G.
Venue
IEEE Transactions on Industry Applications
Published
2007
Volume
43
Issue
4
Pages
920-933
ISSN
0093-9994 (ISSN-L)
OpenAlex
W2026743245
Cited by
301
References
0
FWCI
29.30
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: A Survey of Methods for Detection of Stator-Related Faults in Induction Machines. https://app.cochat.ai/papers/10.1109/tia.2007.900448
The paper
Tallam, R. M., Lee, S. B., Stone, G. C., Kliman, G. B., Yoo, J., Habetler, T. G., & Harley, R. G. (2007). A Survey of Methods for Detection of Stator-Related Faults in Induction Machines. IEEE Transactions on Industry Applications. https://doi.org/10.1109/tia.2007.900448

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.