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

By Xiaofeng Yuan, Biao Huang, Yalin Wang, Chunhua Yang, W. Gui. IEEE Transactions on Industrial Informatics, 2018

540 citations recorded by the source corpus.

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 for output prediction purpose, traditional deep learning algorithms cannot extract high-level output-related features. Thus, a novel variable-wise weighted stacked autoencoder (VW-SAE) is proposed for hierarchical output-related feature representation layer by layer. By correlation analysis with the output variable, important variables are identified from other ones in the input layer of each autoencoder. The variables are assigned with different weights accordingly. Then, variable-wise weighted autoencoders are designed and stacked to form deep networks. An industrial application shows that the proposed VW-SAE can give better prediction performance than the traditional multilayer neural networks and SAE.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f18b10c2817c5e2acfe26d3294832e58fecf213d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhiqiang Ge
2. Qingqiang Sun

In Control and Systems Engineering

1. Lei Ren
2. Zhiqiang Ge
3. Li Zhao
4. Qingqiang Sun

Citations in the corpus

• A Survey on Deep Learning for Data-Driven Soft Sensors (2021, methodology): “In [86], a variablewise weighted SAE was proposed to introduce the linear Pearson coefficient between the inputs of”
• Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development (2020, unknown): “Deep neural networks have also been introduced for soft sensor modeling because of their better performance [19]–[27].”
• A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life (2021, background): “Duetoits deeparchitecture, aDNN model consists of multiple network layers, which can be used to obtain features of the raw data from linear and nonlinear operations [26].”