The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network
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
Support vector machine in machine condition monitoring and fault diagnosis
Widodo, A., & Yang, B.-S. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.12.007 · Cited by 1,650
Cited by
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634
A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
Rai, A., & Upadhyay, S. H. (2016) · Tribology International · doi:10.1016/j.triboint.2015.12.037 · Cited by 804
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey
Zhang, W., Yang, D., & Wang, H. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2905565 · Cited by 732
A review of wind turbine bearing condition monitoring: State of the art and challenges
de Azevedo, H. D. M., Araújo, A. M., & Bouchonneau, N. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.032 · Cited by 382
Remaining useful life prediction of aircraft engine based on degradation pattern learning
Zhao, Z., Liang, B., Wang, X., & Lu, W. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.02.007 · Cited by 267
Metadata
- Authors
- Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 56-57
- Pages
- 150-172
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W2014685668
- Cited by
- 502
- References
- 1
- FWCI
- 22.33
- 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: Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. https://app.cochat.ai/papers/10.1016/j.ymssp.2014.10.014
- The paper
- Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2014.10.014
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.
