The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery
Open accessAbstract
Fault diagnosis of rotating machinery plays a significant role in the industrial production and engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as heavily dependence on human knowledge and professional experience, intelligent fault diagnosis based on deep learning (DL) has aroused the interest of researchers. DL achieves the desirable automatic feature learning and fault classification. Therefore, in this review, DL and DL-based intelligent fault diagnosis techniques are overviewed. DL-based fault diagnosis approaches for rotating machinery are summarized and discussed, primarily including bearing, gear/gearbox and pumps. Finally, with respect to modern intelligent fault diagnosis, the existing challenges and possible future research orientations are prospected and analyzed.
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
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
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
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
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · cited 1×
“AI approaches have been integrated into many various fields, great achievements have been obtained in man-machine game, pattern recognition, automatic engineering and knowledge engineering [43]–[45].”
BackgroundGao, Z., Cecati, C., & Ding, S. X. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2417501 · Cited by 3,135
Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review
Nandi, S., Toliyat, H. A., & Li, X. (2005) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2005.847955 · Cited by 2,382
Cited by
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhu, Z., Lei, Y., Qi, G., Chai, Y., Mazur, N., An, Y., & Huang, X. (2023) · Measurement · doi:10.1016/j.measurement.2022.112346 · Cited by 731
Metadata
- Authors
- Tang, S., Yuan, S., & Zhu, Y.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 9335-9346
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2997353220
- Cited by
- 323
- References
- 113
- FWCI
- 15.68
- Type
- journal article
- Language
- en
- Open access
- gold
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: Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery. https://app.cochat.ai/papers/10.1109/access.2019.2963092
- The paper
- Tang, S., Yuan, S., & Zhu, Y. (2020). Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery. IEEE Access. https://doi.org/10.1109/access.2019.2963092
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.
