Deep generative model with time series-image encoding for manufacturing fault detection in die casting process
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DOI: 10.1007/s10845-022-01981-6
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- Sebastian Larsen & Paul A. Hooper, 2022. "Deep semi-supervised learning of dynamics for anomaly detection in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 457-471, February.
- Maciej Grzenda & Andres Bustillo, 2019. "Semi-supervised roughness prediction with partly unlabeled vibration data streams," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 933-945, February.
- Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
- Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
- Seokho Kang, 2020. "Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 319-326, February.
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Cited by:
- Jiayang Liu & Fuqi Xie & Qiang Zhang & Qiucheng Lyu & Xiaosun Wang & Shijing Wu, 2024. "A multisensory time-frequency features fusion method for rotating machinery fault diagnosis under nonstationary case," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3197-3217, October.
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Keywords
Fault detection; Generative adversarial network; Variational autoencoder; Time series data; Image encoding; Semi-supervised learning;All these keywords.
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