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Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
Abstract
Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.
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Cited by
Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
Deep learning and its applications to machine health monitoring
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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
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Deep learning for smart manufacturing: Methods and applications
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Metadata
- Authors
- Ince, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2016-11
- Volume
- 63
- Issue
- 11
- Pages
- 7067-7075
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2461729787
- Cited by
- 1,407
- References
- 2
- FWCI
- 72.67
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ince, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M. (2016). Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2016.2582729
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