The DOI matches its Crossref record.
Differential Diagnosis of Gear and Bearing Faults
Abstract
This paper deals with the vibration-based diagnosis of rolling element bearings in the presence of strong interfering gear signals, such as is typical of helicopter gearboxes. The key idea consists in recognizing gear signals as purely periodic, whereas bearing signals experience some randomness and are close to cyclostationary, i.e. with a periodic bivariate autocorrelation function. This assertion is demonstrated by introducing a comprehensive model for the vibration generating process of bearing faults: distinctions are made between localized and distributed faults, between cyclostationary and pseudo-cyclostationary processes, and between additive and multiplicative interactions with gear signals. Finally, an original diagnostic procedure is proposed and its performance illustrated using simulated, experimental and actual cases.
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
Rolling element bearing diagnostics—A tutorial
Randall, R. B., & Antoni, J. (2011) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2010.07.017 · Cited by 2,744
Self-citationMoshrefzadeh, A., & Fasana, A. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.12.009 · Cited by 459
Artificial neural networks and genetic algorithm for bearing fault detection
Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2006) · Soft Computing · doi:10.1007/s00500-005-0481-0 · Cited by 182
Vibration based Fault Diagnosis Techniques for Rotating Mechanical Components: Review Paper
Kumar, S., Lokesha, M., Kumar, K., & Srinivas, K. R. (2018) · IOP Conference Series: Materials Science and Engineering · doi:10.1088/1757-899x/376/1/012109 · Cited by 29
Metadata
- Authors
- Antoni, J., & Randall, R. B.
- Venue
- Journal of Vibration and Acoustics
- Publisher
- ASME International
- Published
- 2002-03-26
- Volume
- 124
- Issue
- 2
- Pages
- 165-171
- ISSN
- 0739-3717 (ISSN-L)
- 1048-9002
- 1528-8927
- OpenAlex
- W2003202593
- Cited by
- 361
- References
- 0
- FWCI
- 14.45
- 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: Differential Diagnosis of Gear and Bearing Faults. https://app.cochat.ai/papers/10.1115/1.1456906
- The paper
- Antoni, J., & Randall, R. B. (2002). Differential Diagnosis of Gear and Bearing Faults. Journal of Vibration and Acoustics. https://doi.org/10.1115/1.1456906
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.
