New mode cold start monitoring in industrial processes: A solution of spatial–temporal feature transfer

Wang, Kai; Zhou, Wen-Xuan; Yuan, Xiaofeng; Wang, Yalin; Yang, Chunhua

doi:10.1016/j.knosys.2022.108851

Cited by 11 publications

(1 citation statement)

References 31 publications

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“…As a branch of machine learning, transfer learning (TL) aims to tackle the problem of insufficient training data between domains. − By leveraging knowledge from the related domain having relatively sufficient annotated samples, the predicted target domain having limited labeled samples is expanded. Recently, with good feature extraction capability, deep transfer learning methods − attempt to transfer knowledge.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

et al. 2023

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Although recent transfer learning soft sensors show promising applications in multigrade chemical processes, good prediction performance mainly relies on available target domain data, which is difficult to achieve for a start-up grade. Additionally, only employing a single global model is inadequate to characterize the inner relationship of process variables. A just-in-time adversarial transfer learning (JATL) soft sensing method is developed to enhance multigrade process prediction performance. The distribution discrepancies of process variables between two different operating grades are first reduced by the ATL strategy. Subsequently, by applying the just-in-time learning approach, a similar data set is selected from the transferred source data for reliable model construction. Consequently, with the JATL-based soft sensor, quality prediction of a new target grade is implemented without its own labeled data. Experimental results on two multigrade chemical processes validate that the JATL method can give rise to the improvement of model performance.

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