The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
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
ImageNet 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
(2008) · Journal of Machine Learning Research · Cited by 33,051
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,630
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Cited by
Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894
Self-citationRemaining 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
Deep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740
Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
Shao, S., McAleer, S., Yan, R., & Baldi, P. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2864759 · Cited by 1,635
Self-citationA review on the application of deep learning in system health management
Khan, S., & Yairi, T. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.024 · Cited by 1,133
Metadata
- Authors
- Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., & Chen, X.
- Venue
- Measurement
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 89
- Pages
- 171-178
- ISSN
- 0263-2241 (ISSN-L)
- OpenAlex
- W2324044936
- Cited by
- 698
- References
- 33
- FWCI
- 59.48
- 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 sparse auto-encoder-based deep neural network approach for induction motor faults classification. https://app.cochat.ai/papers/10.1016/j.measurement.2016.04.007
- The paper
- Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., & Chen, X. (2016). A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement. https://doi.org/10.1016/j.measurement.2016.04.007
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.
