Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring

Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in chemical plants with faults that cause quality problems. Traditional monitoring strategies are limitedly applied in chemical plants because qu...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Neural networks Ročník 136; s. 54 - 62
Hlavní autori:	Wang, Kai, Yuan, Xiaofeng, Chen, Junghui, Wang, Yalin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.04.2021
Predmet:	Algorithms Models, Statistical Neural Networks, Computer Nonlinear process monitoring Quality-relevant monitoring Semi-supervised variational autoencoders Supervised Machine Learning - standards Supervised variational autoencoders Nonlinear process monitoring Supervised variational autoencoders Quality-relevant monitoring Semi-supervised variational autoencoders
ISSN:	0893-6080, 1879-2782, 1879-2782
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in chemical plants with faults that cause quality problems. Traditional monitoring strategies are limitedly applied in chemical plants because quality targets in training data are insufficient. It is hard for inflexible models to fully capture the strongly nonlinear process-quality correlations. Also, deterministic models are mapped from process variables to qualities without any consideration of uncertainties. Simultaneously, a slow sampling rate for quality variables is ubiquitous in chemical plants since a product quality test is often time-consuming and expensive. Motivated by these limitations, this paper proposes a new concurrent process-quality monitoring scheme based on a probabilistic generative deep learning model developed from variational autoencoder. The supervised model is firstly developed and then the semi-supervised version is extended to solve the issue of missing targets. Especially, the semi-supervised learning algorithm is accomplished with an optimal parameter estimation in the light of maximum likelihood principle and no any hyperparameters are introduced. Two case studies validate that the proposed method effectively outperforms the other comparative methods in concurrent process-quality monitoring. •A probabilistic deep network for learning correlations between process and quality.•The semi-supervised network dealing with slow-sampling quality variables.•Unified framework integrating supervised and semi-supervised network training.•Application of concurrent process-quality monitoring.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0893-6080 1879-2782 1879-2782
DOI:	10.1016/j.neunet.2020.11.006