The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals
Abstract
The objective of this paper is to provide a review of recent advances in automatic vibration- and audio-based fault diagnosis in machinery using condition monitoring strategies. It presents the most valuable techniques and results in this field and highlights the most profitable directions of research to present. Automatic fault diagnosis systems provide greater security in surveillance of strategic infrastructures, such as electrical substations and industrial scenarios, reduce downtime of machines, decrease maintenance costs, and avoid accidents which may have devastating consequences. Automatic fault diagnosis systems include signal acquisition, signal processing, decision support, and fault diagnosis. The paper includes a comprehensive bibliography of more than 100 selected references which can be used by researchers working in this field.
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Citations
Cites
A review on machinery diagnostics and prognostics implementing condition-based maintenance
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2005.09.012 · Cited by 4,816
A Summary Review of Vibration-Based Damage Identification Methods
Doebling, S. W., Farrar, C. R., & Prime, M. B. (1998) · The Shock and Vibration Digest · doi:10.1177/058310249803000201 · Cited by 2,823
Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review
Nandi, S., Toliyat, H. A., & Li, X. (2005) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2005.847955 · Cited by 2,382
Tandon, N., & Choudhury, A. (1999) · Tribology International · doi:10.1016/s0301-679x(99)00077-8 · Cited by 1,351
Antoni, J., & Randall, R. B. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2004.09.002 · Cited by 1,333
Intelligent Fault Diagnosis and Prognosis for Engineering Systems
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006) · Wiley eBooks · doi:10.1002/9780470117842 · Cited by 1,134
Peng, Z. K., Tse, P. W., & Chu, F. L. (2005) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2004.01.006 · Cited by 840
McFadden, P. D., & Smith, J. D. (1984) · Tribology International · doi:10.1016/0301-679x(84)90076-8 · Cited by 813
Application of the EEMD method to rotor fault diagnosis of rotating machinery
Lei, Y., He, Z., & Zi, Y. (2009) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2008.11.005 · Cited by 587
Fault diagnosis of ball bearings using machine learning methods
Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.07.119 · Cited by 497
Cited by
Mel Frequency Cepstral Coefficient and its Applications: A Review
Abdul, Z. K., & Al-Talabani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3223444 · Cited by 527
Bayesian Networks in Fault Diagnosis
Cai, B., Huang, L., & Xie, M. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2695583 · Cited by 474
Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308
Saucedo-Dorantes, J. J., Delgado-Prieto, M., Ortega-Redondo, J. A., Osornio-Rios, R. A., & Romero-Troncoso, R. D. J. (2016) · Shock and Vibration · doi:10.1155/2016/5467643 · Cited by 80
Metadata
- Authors
- Henriquez, P., Alonso, J. B., Ferrer, M. A., & Travieso, C. M.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2014-05
- Volume
- 44
- Issue
- 5
- Pages
- 642-652
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2019055402
- Cited by
- 314
- References
- 65
- FWCI
- 9.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Henriquez, P., Alonso, J. B., Ferrer, M. A., & Travieso, C. M. (2014). Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmcc.2013.2257752
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.
