The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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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
Wavelets for fault diagnosis of rotary machines: A review with applications
Yan, R., Gao, R. X., & Chen, X. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.04.015 · Cited by 1,446
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817
Deep Learning using Linear Support Vector Machines
Tang, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.0239 · Cited by 708
Failure diagnosis using deep belief learning based health state classification
Tamilselvan, P., & Wang, P. (2013) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2013.02.022 · Cited by 630
An Efficient Learning Procedure for Deep Boltzmann Machines
Salakhutdinov, R., & Hinton, G. (2012) · Neural Computation · doi:10.1162/neco_a_00311 · Cited by 484
Tran, V. T., AlThobiani, F., & Ball, A. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.026 · Cited by 386
Li, C., Sanchez, R.-V., Zurita, G., Cerrada, M., Cabrera, D., & Vásquez, R. E. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.06.008 · Cited by 295
Self-citation
Cited by
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
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
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
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey
Zhang, W., Yang, D., & Wang, H. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2905565 · Cited by 732
Jing, L., Zhao, M., Li, P., & Xu, X. (2017) · Measurement · doi:10.1016/j.measurement.2017.07.017 · Cited by 698
Metadata
- Authors
- Li, C., Sanchez, R.-V., Zurita, G., Cerrada, M., Cabrera, D., & Vásquez, R. E.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 76-77
- Pages
- 283-293
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W2287029277
- Cited by
- 470
- References
- 33
- FWCI
- 36.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. https://app.cochat.ai/papers/10.1016/j.ymssp.2016.02.007
- The paper
- Li, C., Sanchez, R.-V., Zurita, G., Cerrada, M., Cabrera, D., & Vásquez, R. E. (2016). Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2016.02.007
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.
