The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image
Abstract
Abstract Deep learning theory has been widely used for diagnosing bearing faults. However, this method still has same drawbacks. For example, single time or frequency domain analysis methods cannot effectively extract features, the ReLU function is greatly affected by the learning rate, and it is difficult to achieve satisfactory results using the same regularization for different layers. To overcome the aforementioned deficiencies: (1) short-time Fourier transform theory to obtain an input image, (2) the scaled exponential linear unit (SELU) function is introduced to avoid excessive “dead” nodes during the training process, and (3) the use of hierarchical regularization to obtain better training results. Small sample datasets were used for the test experiment in two bearing fault simulators. The experiment results showed that the proposed method has a higher fault diagnosis accuracy than existing deep learning diagnosis methods.
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
(2008) · Journal of Machine Learning Research · Cited by 33,051
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
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
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
Face recognition: a convolutional neural-network approach
Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.554195 · Cited by 3,092
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
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
A new convolutional neural network-based data-driven fault diagnosis method
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2774777 · Cited by 2,222
Self-citation
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
Jalayer, M., Orsenigo, C., & Vercellis, C. (2021) · Computers in Industry · doi:10.1016/j.compind.2020.103378 · Cited by 382
Wang, H., Liu, Z., Peng, D., & Zuo, M. J. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110314 · Cited by 270
Metadata
- Authors
- Zhang, Y., Xing, K., Bai, R., Sun, D., & Meng, Z.
- Venue
- Measurement
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 157
- Pages
- 107667
- ISSN
- 0263-2241 (ISSN-L)
- OpenAlex
- W3008819860
- Cited by
- 283
- References
- 40
- FWCI
- 21.23
- 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: An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. https://app.cochat.ai/papers/10.1016/j.measurement.2020.107667
- The paper
- Zhang, Y., Xing, K., Bai, R., Sun, D., & Meng, Z. (2020). An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. Measurement. https://doi.org/10.1016/j.measurement.2020.107667
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.
