The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Self-citationImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 14,214
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Self-citationIdentity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
Self-citationSmith, W. A., & Randall, R. B. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.04.021 · Cited by 2,719
Remaining useful life estimation in prognostics using deep convolution neural networks
Li, X., Ding, Q., & Sun, J.-Q. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.11.021 · Cited by 1,755
Self-citationQiu, H., Lee, J., Lin, J., & Yu, G. (2006) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2005.03.007 · Cited by 1,477
Cited by
Zhao, Z., Li, T., Wu, J., Sun, C., Wang, S., Yan, R., & Chen, X. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.08.010 · Cited by 688
Saufi, S. R., Ahmad, Z. A. B., Leong, M. S., & Lim, M. H. (2019) · IEEE Access · doi:10.1109/access.2019.2938227 · Cited by 312
Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning
Li, X., Zhang, W., Ding, Q., & Li, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2927590 · Cited by 298
Self-citationXie, J., Sun, L., & Zhao, Y. F. (2025) · Engineering · doi:10.1016/j.eng.2024.04.024 · Cited by 112
Metadata
- Authors
- Li, X., Zhang, W., Ding, Q., & Sun, J.-Q.
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2018-11-30
- Volume
- 31
- Issue
- 2
- Pages
- 433-452
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W2902985761
- Cited by
- 404
- References
- 40
- FWCI
- 17.53
- 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: Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. https://app.cochat.ai/papers/10.1007/s10845-018-1456-1
- The paper
- Li, X., Zhang, W., Ding, Q., & Sun, J.-Q. (2020). Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1456-1
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.
