A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance
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DOI: 10.1007/s10845-021-01810-2
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- Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
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- Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
- Wo Jae Lee & Gamini P. Mendis & Matthew J. Triebe & John W. Sutherland, 2020. "Monitoring of a machining process using kernel principal component analysis and kernel density estimation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1175-1189, June.
- A. Khatab, 2018. "Maintenance optimization in failure-prone systems under imperfect preventive maintenance," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 707-717, March.
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Keywords
Process drift; Fault detection; Incomplete maintenance; Variational autoencoder; Deep learning;All these keywords.
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