[HTML][HTML] A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes
Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …
processes. However, with the increasing complexity of modern industrial processes …
Learning deep multimanifold structure feature representation for quality prediction with an industrial application
Due to the existence of complex disturbances and frequent switching of operational
conditions characteristics in the real industrial processes, the process data under different …
conditions characteristics in the real industrial processes, the process data under different …
A novel unsupervised approach for batch process monitoring using deep learning
Process monitoring is an important tool used to ensure safe operation of a process plant and
to maintain high quality of end products. The focus of this work is on unsupervised Statistical …
to maintain high quality of end products. The focus of this work is on unsupervised Statistical …
A batch-wise LSTM-encoder decoder network for batch process monitoring
Process monitoring is essential to keep quality consistency and operation safety in the batch
process. However, the existence of multiphase, nonlinearity and dynamic features in the …
process. However, the existence of multiphase, nonlinearity and dynamic features in the …
A fast ramp-up framework for wafer yield improvement in semiconductor manufacturing systems
Abstracts Wafer yield is crucial for assessing semiconductor fabrication enterprises' stability
and technological maturity. Quickly achieving the yield ramp-up of new products and timely …
and technological maturity. Quickly achieving the yield ramp-up of new products and timely …
Data-driven adaptive virtual metrology for yield prediction in multibatch wafers
Virtual metrology (VM) is widely used for yield management and control in semiconductor
manufacturing owing to its high real-time inspection, low cost, and convenient maintenance …
manufacturing owing to its high real-time inspection, low cost, and convenient maintenance …
Multi-scale data-driven engineering for biosynthetic titer improvement
Industrial biosynthesis is a very complex process which depends on a range of different
factors, from intracellular genes and metabolites, to extracellular culturing conditions and …
factors, from intracellular genes and metabolites, to extracellular culturing conditions and …
Probabilistic stationary subspace analysis for monitoring nonstationary industrial processes with uncertainty
D Wu, D Zhou, M Chen - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
Actual industrial processes often show nonstationary characteristics, so nonstationary
process monitoring is significant to ensure the safety and reliability of industrial processes …
process monitoring is significant to ensure the safety and reliability of industrial processes …
Sparsity and manifold regularized convolutional auto-encoders-based feature learning for fault detection of multivariate processes
C Zhang, J Yu, L Ye - Control Engineering Practice, 2021 - Elsevier
Deep neural networks (DNNs) are popular in process monitoring for its remarkable feature
extraction from data. However, the increased dimension and correlation of the process …
extraction from data. However, the increased dimension and correlation of the process …
Deep discriminative representation learning for nonlinear process fault detection
Nonlinear process fault detection remains a challenge, with representation learning being a
key step. In this article, a deep neural network (DNN)-based discriminative representation …
key step. In this article, a deep neural network (DNN)-based discriminative representation …