The DOI matches its Crossref record; 117 of the 117 references it cites were found in a registry or an index.
Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review
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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S. E., et al. (2020) · doi:10.31222/osf.io/v7gm2 · Cited by 5,177
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003583 · Cited by 5,148
Multisensor data fusion: A review of the state-of-the-art
Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013) · Information Fusion · doi:10.1016/j.inffus.2011.08.001 · Cited by 2,074
A Review of Data Fusion Techniques
Castanedo, F. (2013) · The Scientific World Journal · doi:10.1155/2013/704504 · Cited by 1,027
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
Jiang, G., He, H., Yan, J., & Xie, P. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2844805 · Cited by 892
Chen, Z., & Li, W. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2669947 · Cited by 883
Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks
Xia, M., Li, T., Xu, L., Liu, L., & de Silva, C. W. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2728371 · Cited by 723
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
Shao, H., Jiang, H., Zhao, H., & Wang, F. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.03.034 · Cited by 684
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
Tian, J., Morillo, C., Azarian, M. H., & Pecht, M. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2509913 · Cited by 488
Metadata
- Authors
- Kibrete, F., Engida Woldemichael, D., & Shimels Gebremedhen, H.
- Venue
- Measurement
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 232
- Pages
- 114658
- ISSN
- 0263-2241 (ISSN-L)
- OpenAlex
- W4393942356
- Cited by
- 270
- References
- 117
- FWCI
- 42.73
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review. https://app.cochat.ai/papers/10.1016/j.measurement.2024.114658
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- Kibrete, F., Engida Woldemichael, D., & Shimels Gebremedhen, H. (2024). Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review. Measurement. https://doi.org/10.1016/j.measurement.2024.114658
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.
