The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
A theory for multiresolution signal decomposition: the wavelet representation
Mallat, S. G. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,372
Wu, Y., & He, K. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01198-w · Cited by 3,601
Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
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
Zhang, W., Li, C., Peng, G., Chen, Y., & Zhang, Z. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.06.022 · Cited by 1,421
Lessmeier, C., Kimotho, J. K., Zimmer, D., & Sextro, W. (2016) · PHM Society European Conference · doi:10.36001/phme.2016.v3i1.1577 · Cited by 1,268
A review on data-driven fault severity assessment in rolling bearings
Cerrada, M., Sánchez, R.-V., Li, C., Pacheco, F., Cabrera, D., Valente de Oliveira, J., & Vásquez, R. E. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.06.012 · Cited by 724
Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network
Wang, X., Mao, D., & Li, X. (2021) · Measurement · doi:10.1016/j.measurement.2020.108518 · Cited by 641
Metadata
- Authors
- Wang, H., Liu, Z., Peng, D., & Zuo, M. J.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 195
- Pages
- 110314
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W4361294415
- Cited by
- 270
- References
- 31
- FWCI
- 29.35
- 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: Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis. https://app.cochat.ai/papers/10.1016/j.ymssp.2023.110314
- The paper
- Wang, H., Liu, Z., Peng, D., & Zuo, M. J. (2023). Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2023.110314
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.
