The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification
Abstract
The key challenge of intelligent fault diagnosis is to develop features that can distinguish different categories. Because of the unique properties of mechanical data, predetermined features based on prior knowledge are usually used as inputs for fault classification. However, proper selection of features often requires expertise knowledge and becomes more difficult and time consuming when volume of data increases. In this paper, a novel deep learning network (LiftingNet) is proposed to learn features adaptively from raw mechanical data without prior knowledge. Inspired by convolutional neural network and second generation wavelet transform, the LiftingNet is constructed to classify mechanical data even though inputs contain considerable noise and randomness. The LiftingNet consists of split layer, predict layer, update layer, pooling layer, and full-connection layer. Different kernel sizes are allowed in convolutional layers to improve learning ability. As a multilayer neural network, deep features are learned from shallow ones to represent complex structures in raw data. Feasibility and effectiveness of the LiftingNet is validated by two motor bearing datasets. Results show that the proposed method could achieve layerwise feature learning and successfully classify mechanical data even with different rotating speed and under the influence of random noise.
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Cited by
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
Ahmed, I., Jeon, G., & Piccialli, F. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3146552 · Cited by 853
Shao, H., Xia, M., Han, G., Zhang, Y., & Wan, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3005965 · Cited by 452
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019) · Sensors · doi:10.3390/s20010109 · Cited by 365
Jiao, J., Zhao, M., Lin, J., & Zhao, J. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.07.017 · Cited by 127
Metadata
- Authors
- Pan, J., Zi, Y., Chen, J., Zhou, Z., & Wang, B.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2018-06
- Volume
- 65
- Issue
- 6
- Pages
- 4973-4982
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2765284480
- Cited by
- 294
- References
- 7
- FWCI
- 18.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pan, J., Zi, Y., Chen, J., Zhou, Z., & Wang, B. (2018). LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2017.2767540
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