A review on data-driven process monitoring methods: Characterization and mining of industrial data
Safe and stable operation plays an important role in the chemical industry. Fault detection
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …
A supervised functional Bayesian inference model with transfer-learning for performance enhancement of monitoring target batches with limited data
To increase the monitoring performance of the batch process with serious nonlinearity,
uneven-length, and limited-data issues, a supervised transfer-learning based functional …
uneven-length, and limited-data issues, a supervised transfer-learning based functional …
Wavelet functional principal component analysis for batch process monitoring
To facilitate the understanding and analysis of process conditions, a novel wavelet
functional principal component analysis is proposed for monitoring batch processes from the …
functional principal component analysis is proposed for monitoring batch processes from the …
Soft sensor framework based on semisupervised just-in-time relevance vector regression for multiphase batch processes with unlabeled data
K Qiu, J Wang, X Zhou, Y Guo… - Industrial & Engineering …, 2020 - ACS Publications
Soft sensors using just-in-time learning (JITL) have attracted much attention in the
application of online prediction in batch processes because of the ability to perform adaptive …
application of online prediction in batch processes because of the ability to perform adaptive …
Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process
Q Qian, X Fang, J Xu, M Li - Journal of MAnufacturing Systems, 2021 - Elsevier
Many industrial processes are equipped with a large number of sensors, which usually
generate multichannel high-dimensional profiles that can be used to monitor the health …
generate multichannel high-dimensional profiles that can be used to monitor the health …
Industrial process monitoring based on dynamic overcomplete broad learning network
C Peng, X Ying, H ZhiQi - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Most industrial processes feature high nonlinearity, non-Gaussianity, and time correlation.
Models based on overcomplete broad learning system (OBLS) have been successfully …
Models based on overcomplete broad learning system (OBLS) have been successfully …
Development of soft sensor based on sequential kernel fuzzy partitioning and just-in-time relevance vector machine for multiphase batch processes
J Wang, K Qiu, R Wang, X Zhou… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Batch processes are important manufacturing approaches widely used in modern industry.
During the manufacturing process, quality prediction is essential. Data-driven-based soft …
During the manufacturing process, quality prediction is essential. Data-driven-based soft …
Monitoring framework based on generalized tensor PCA for three-dimensional batch process data
Unfolding is a pretreating operation in most existing batch process modeling methods, but it
would destroy the essential structure of the raw data. Also, the number of estimated …
would destroy the essential structure of the raw data. Also, the number of estimated …
Comparative study on wavelet functional partial least squares soft sensor for complex batch processes
Conventional data-driven models for batch processes conduct unfolding operations and
neglect the continuous property. A novel soft sensor method is proposed based on the …
neglect the continuous property. A novel soft sensor method is proposed based on the …
Recursive Gaussian mixture models for adaptive process monitoring
Gaussian mixture models (GMM) have recently been introduced and widely used for
process monitoring. This paper intends to develop a new recursive GMM model for adaptive …
process monitoring. This paper intends to develop a new recursive GMM model for adaptive …