The DOI matches its Crossref record; 198 of the 198 references it cites were found in a registry or an index.
Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review
Open accessAbstract
In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed.
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(2014) · Cited by 33,419
(2008) · Journal of Machine Learning Research · Cited by 33,051
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 19,343
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
(2012) · Cited by 8,009
Natural Language Processing (almost) from Scratch
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 4,962
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
Mirjalili, S. (2015) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2015.01.010 · Cited by 3,233
Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894
Deep learning for visual understanding: A review
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504
Cited by
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
Automatic Classification of Rotating Machinery Defects Using Machine Learning (ML) Algorithms
Zoungrana, W.-B., Chehri, A., & Zimmermann, A. (2021) · Smart Innovation, Systems and Technologies · doi:10.1007/978-981-15-5784-2_16 · Cited by 8
Metadata
- Authors
- Saufi, S. R., Ahmad, Z. A. B., Leong, M. S., & Lim, M. H.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 122644-122662
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2970706158
- Cited by
- 312
- References
- 198
- FWCI
- 13.60
- Type
- journal article
- Language
- en
- Open access
- gold
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