Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

@article{Yuan2018DeepLF,
  title={Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE},
  author={Xiaofeng Yuan and Biao Huang and Yalin Wang and Chunhua Yang and Weihua Gui},
  journal={IEEE Transactions on Industrial Informatics},
  year={2018},
  volume={14},
  pages={3235-3243}
}
In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate soft sensors. Recently, deep learning techniques have been developed for high-level abstract feature extraction in pattern recognition areas, which also have great potential for soft sensing applications. Hence, deep stacked autoencoder (SAE) is introduced for soft sensor in this paper. As… Expand
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References

SHOWING 1-10 OF 29 REFERENCES
Data-driven soft sensor development based on deep learning technique
Abstract In industrial process control, some product qualities and key variables are always difficult to measure online due to technical or economic limitations. As an effective solution, data-drivenExpand
A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application
TLDR
This paper proposes a novel soft sensor modeling method based on a deep learning network that integrates denoising autoencoders with a neural network (DAE-NN), and is able to capture the essential information of input data through deep architecture, building soft sensors with excellent performance. Expand
Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application
  • Le Yao, Zhiqiang Ge
  • Engineering, Computer Science
  • IEEE Transactions on Industrial Electronics
  • 2018
TLDR
The proposed semisupervised HELM method is applied in a high–low transformer to estimate the carbon monoxide content, which shows a significant improvement of the prediction accuracy, compared to traditional methods. Expand
Weighted Linear Dynamic System for Feature Representation and Soft Sensor Application in Nonlinear Dynamic Industrial Processes
TLDR
In this paper, a novel weighted linear dynamic system (WLDS) is proposed for nonlinear dynamic feature extraction and two kinds of weights are proposed for local linearization of the nonlinear state evolution and state emission relationships. Expand
Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes
The principal component regression (PCR) based soft sensor modeling technique has been widely used for process quality prediction in the last decades. While most industrial processes areExpand
Application of Online Support Vector Regression for Soft Sensors
Soft sensors have been widely used in chemical plants to estimate process variables that are difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracyExpand
A late fusion approach for harnessing multi-cnn model high-level features
TLDR
The experimental outcomes demonstrate that regardless of variation in visual statistics and tasks the fusion of multi-ConvNets' high-level features can meliorate the classification accuracy compared with a single modality and the fusion is capable of producing a very competitive performance to the state-of-the-art methods. Expand
Online Mixture of Univariate Linear Regression Models for Adaptive Soft Sensors
TLDR
This paper proposes a mixture of univariate linear regression models (MULRM) to be applied in time-varying scenarios, and its application to soft sensor problems, and the proposed method always exhibits the best prediction performance. Expand
Soft Sensor Modeling of Nonlinear Industrial Processes Based on Weighted Probabilistic Projection Regression
TLDR
A novel weighted PPCR (WPPCR) algorithm is proposed in this paper for soft sensing of nonlinear processes and its effectiveness and flexibility are validated on a numerical example and an industrial process. Expand
Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR
TLDR
A novel semisupervised JITL framework is proposed for soft sensor modeling for nonlinear processes, which is based on semisuperedvised weighted probabilistic principal component regression (SWPPCR) and the effectiveness and flexibility of the proposed method are demonstrated. Expand
...
1
2
3
...