The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Bearing Damage Detection via Wavelet Packet Decomposition of the Stator Current
Abstract
Bearing faults are one of the major causes of motor failures. The bearing defects induce vibration, resulting in the modulation of the stator current. In this paper, the stator current is analyzed via wavelet packet decomposition to detect bearing defects. The proposed method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies. The wavelet packet decomposition also provides a better treatment of nonstationary stator current than currently used Fourier techniques.
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
Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
Cited by
A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier
Eren, L., Ince, T., & Kiranyaz, S. (2019) · Journal of Signal Processing Systems · doi:10.1007/s11265-018-1378-3 · Cited by 723
Self-citationTransfer learning with neural networks for bearing fault diagnosis in changing working conditions
Zhang, R., Tao, H., Wu, L., & Guan, Y. (2017) · IEEE Access · doi:10.1109/access.2017.2720965 · Cited by 378
Bearing fault detection by one-dimensional convolutional neural networks
Eren, L. (2017) · Mathematical Problems in Engineering · doi:10.1155/2017/8617315 · Cited by 250
Self-citation
Metadata
- Authors
- Eren, L., & Devaney, M. J.
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2004-04
- Volume
- 53
- Issue
- 2
- Pages
- 431-436
- ISSN
- 0018-9456 (ISSN-L)
- OpenAlex
- W2108744642
- Cited by
- 282
- References
- 1
- FWCI
- 22.84
- 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: Bearing Damage Detection via Wavelet Packet Decomposition of the Stator Current. https://app.cochat.ai/papers/10.1109/tim.2004.823323
- The paper
- Eren, L., & Devaney, M. J. (2004). Bearing Damage Detection via Wavelet Packet Decomposition of the Stator Current. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2004.823323
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.