Stacked Dual-Guided Autoencoder: A Scalable Deep Latent Variable Model for Semi-Supervised Industrial Soft Sensing

Stacked autoencoders (SAEs) have been widely used in soft sensing of industrial process data. However, most of them extract features by minimizing the reconstruction error of the input data at each layer, leading to accumulated loss. Moreover, the extraction of quality-related features is crucial fo...

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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 14
Hlavní autori:	Yang, Zeyu, Hu, Tingting, Yao, Le, Ye, Lingjian, Qiu, Yi, Du, Shuxin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Data models Decoding Deep learning Encoding Feature extraction feature representation Monitoring Performance prediction Real variables semi-supervised learning soft sensor Soft sensors stacked autoencoder (SAE) Vectors
ISSN:	0018-9456, 1557-9662
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