The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images
Abstract
Abstract The bearings are the crucial components of rotating machines in an industrial firm. Unplanned failure of these components not only increases the downtime, but also leads to production loss. This paper presents a non-invasive thermal image-based method for bearing fault diagnosis in rotating machines. Thermal images of rolling-element bearing in six conditions have been considered, including one healthy and five faulty conditions, and then a comparison based on classification performance has been done using shallow and deep learning approaches incorporating artificial neural network (ANN) and convolutional neural network (CNN). The CNN used in this work is based on the LeNet-5 structure and has proved to be a better than the ANN. It has been concluded that infrared thermography can be used in a non-contact way to automatically identify the faults that help to detect early warnings, irrespective of speeds and hence ensures reduced system shutdowns causing by bearing failure.
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Citations
Cites
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
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
Support vector machine in machine condition monitoring and fault diagnosis
Widodo, A., & Yang, B.-S. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.12.007 · Cited by 1,650
Chen, Z., & Li, W. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2669947 · Cited by 883
Infrared thermography (IRT) applications for building diagnostics: A review
Kylili, A., Fokaides, P. A., Christou, P., & Kalogirou, S. A. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.08.005 · Cited by 526
Pandya, D. H., Upadhyay, S. H., & Harsha, S. P. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.01.033 · Cited by 350
Applications of Thermal Imaging in Agriculture and Food Industry—A Review
Vadivambal, R., & Jayas, D. S. (2011) · Food and Bioprocess Technology · doi:10.1007/s11947-010-0333-5 · Cited by 348
Condition Monitoring and Fault Diagnosis of Induction Motors: A Review
Choudhary, A., Goyal, D., Shimi, S. L., & Akula, A. (2019) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-018-9286-z · Cited by 302
Self-citationPeng, D., Liu, Z., Wang, H., Qin, Y., & Jia, L. (2019) · IEEE Access · doi:10.1109/access.2018.2888842 · Cited by 271
Infrared Thermography-Based Fault Diagnosis of Induction Motor Bearings Using Machine Learning
Choudhary, A., Goyal, D., & Letha, S. S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3015868 · Cited by 245
Self-citation
Cited by
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
Shao, H., Li, W., Cai, B., Wan, J., Xiao, Y., & Yan, S. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3232766 · Cited by 284
Metadata
- Authors
- Choudhary, A., Mian, T., & Fatima, S.
- Venue
- Measurement
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 176
- Pages
- 109196
- ISSN
- 0263-2241 (ISSN-L)
- OpenAlex
- W3131671510
- Cited by
- 292
- References
- 26
- FWCI
- 25.11
- Type
- journal article
- Language
- en
- Open access
- closed
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