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Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
Open accessAbstract
Abstract Vibration-based rolling element bearing diagnostics is a very well-developed field, yet researchers continue to develop new diagnostic algorithms quite frequently. Over the last decade, data from the Case Western Reserve University (CWRU) Bearing Data Center has become a standard reference used to test these algorithms, yet without any recognised benchmark it is difficult to properly assess the performance of any proposed diagnostic methods. There is, then, a clear need to examine the data thoroughly and to categorise it appropriately, and this paper intends to fulfil that objective. To do so, three established diagnostic techniques are applied to the entire CWRU data set, and the diagnostic outcomes are provided and discussed in detail. Recommendations are given as to how the data might best be used, and also on how any future benchmark data should be generated. Though intended primarily as a benchmark to aid in testing new diagnostic algorithms, it is also hoped that much of the discussion will have broader applicability to other bearing diagnostics cases.
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Cited by
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
Convolutional Neural Network Based Fault Detection for Rotating Machinery
Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van de Walle, R., & Van Hoecke, S. (2016) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2016.05.027 · Cited by 1,255
A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier
Eren, L., Ince, T., & Kiranyaz, S. (2019) · Journal of Signal Processing Systems · doi:10.1007/s11265-018-1378-3 · Cited by 723
Moshrefzadeh, A., & Fasana, A. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.12.009 · Cited by 459
Lu, C., Wang, Z., & Zhou, B. (2017) · Advanced Engineering Informatics · doi:10.1016/j.aei.2017.02.005 · Cited by 434
Metadata
- Authors
- Smith, W. A., & Randall, R. B.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 64-65
- Pages
- 100-131
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W243674440
- Cited by
- 2,719
- References
- 0
- FWCI
- 47.02
- Type
- journal article
- Language
- en
- Open access
- green
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- Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2015.04.021
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