The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Open accessAbstract
A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset.
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CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis
Ruan, D., Wang, J., Yan, J., & Gühmann, C. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101877 · Cited by 435
Ding, Y., Zhuang, J., Ding, P., & Jia, M. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108126 · Cited by 253
A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition
Neupane, D., & Seok, J. (2020) · Electronics · doi:10.3390/electronics9111972 · Cited by 192
Self-citation
Metadata
- Authors
- Neupane, D., & Seok, J.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 93155-93178
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3022604663
- Cited by
- 568
- References
- 19
- FWCI
- 29.80
- Type
- journal article
- Language
- en
- Open access
- gold
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