SVAE-WGAN-Based Soft Sensor Data Supplement Method for Process Industry

Challenges of process industry, which is characterized as hugeness of process variables in complexity of industrial environment, can be tackled effectively by the use of soft sensor technology. However, how to supplement the dataset with effective data supplement method under harsh industrial enviro...

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Published in:	IEEE sensors journal Vol. 22; no. 1; pp. 601 - 610
Main Authors:	Gao, Shiwei, Qiu, Sulong, Ma, Zhongyu, Tian, Ran, Liu, Yanxing
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Data models data supplement Datasets Decoding Feature extraction Generative adversarial networks Industries Mathematical models Model accuracy Predictive models Process variables Sensors Soft sensor SVAE-WGAN Training Wasserstein generative adversarial network
ISSN:	1530-437X, 1558-1748
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Abstract	Challenges of process industry, which is characterized as hugeness of process variables in complexity of industrial environment, can be tackled effectively by the use of soft sensor technology. However, how to supplement the dataset with effective data supplement method under harsh industrial environment is a key issue for the enhancement of prediction accuracy in soft-sensing model. Aimed at this problem, a SVAE-WGAN based soft sensor data supplement method is proposed for process industry. Firstly, deep features are extracted with the stacking of the variational autoencoder (SVAE). Secondly, a generation model is constructed with the combination of stacked variational autoencoder (SVAE) and Wasserstein generative adversarial network (WGAN). Thirdly, the proposed model is optimized with training of dataset in industrial process. Finally, the proposed model is evaluated with abundant experimental tests in terms of MSE, RMSE and MAE. It is shown in the results that the proposed SVAE-WGAN generation network is significantly better than that of the traditional VAE, GAN and WGAN generation network in case of industrial steam volume dataset. Specially, the proposed method is more effective than the latest reference VA-WGAN generation network in terms of RMSE, which is enhanced about 9.08% at most. Moreover, the prediction precision of soft sensors could be improved via the supplement of the training samples.
AbstractList	Challenges of process industry, which is characterized as hugeness of process variables in complexity of industrial environment, can be tackled effectively by the use of soft sensor technology. However, how to supplement the dataset with effective data supplement method under harsh industrial environment is a key issue for the enhancement of prediction accuracy in soft-sensing model. Aimed at this problem, a SVAE-WGAN based soft sensor data supplement method is proposed for process industry. Firstly, deep features are extracted with the stacking of the variational autoencoder (SVAE). Secondly, a generation model is constructed with the combination of stacked variational autoencoder (SVAE) and Wasserstein generative adversarial network (WGAN). Thirdly, the proposed model is optimized with training of dataset in industrial process. Finally, the proposed model is evaluated with abundant experimental tests in terms of MSE, RMSE and MAE. It is shown in the results that the proposed SVAE-WGAN generation network is significantly better than that of the traditional VAE, GAN and WGAN generation network in case of industrial steam volume dataset. Specially, the proposed method is more effective than the latest reference VA-WGAN generation network in terms of RMSE, which is enhanced about 9.08% at most. Moreover, the prediction precision of soft sensors could be improved via the supplement of the training samples.
Author	Ma, Zhongyu Tian, Ran Qiu, Sulong Gao, Shiwei Liu, Yanxing
Author_xml	– sequence: 1 givenname: Shiwei orcidid: 0000-0001-9145-2309 surname: Gao fullname: Gao, Shiwei email: gaoshiwei@nwnu.edu.cn organization: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China – sequence: 2 givenname: Sulong surname: Qiu fullname: Qiu, Sulong organization: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China – sequence: 3 givenname: Zhongyu surname: Ma fullname: Ma, Zhongyu organization: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China – sequence: 4 givenname: Ran surname: Tian fullname: Tian, Ran organization: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China – sequence: 5 givenname: Yanxing surname: Liu fullname: Liu, Yanxing organization: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
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SubjectTerms	Data models data supplement Datasets Decoding Feature extraction Generative adversarial networks Industries Mathematical models Model accuracy Predictive models Process variables Sensors Soft sensor SVAE-WGAN Training Wasserstein generative adversarial network
Title	SVAE-WGAN-Based Soft Sensor Data Supplement Method for Process Industry
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