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An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks
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Cites
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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
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Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.025 · Cited by 1,710
A review on the application of deep learning in system health management
Khan, S., & Yairi, T. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.024 · Cited by 1,133
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
Metadata
- Authors
- Tran, V. T., AlThobiani, F., & Ball, A.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2014-07
- Volume
- 41
- Issue
- 9
- Pages
- 4113-4122
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1967879920
- Cited by
- 386
- References
- 25
- FWCI
- 29.03
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tran, V. T., AlThobiani, F., & Ball, A. (2014). An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2013.12.026
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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.
