Synthesizing labeled data to enhance soft sensor performance in data-scarce regions

Lyu, Yuting; Chen, Junghui; Song, Zhihuan

doi:10.1016/j.conengprac.2021.104903

Cited by 14 publications

(7 citation statements)

References 44 publications

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“…In such a situation, a large measurement delay occurs, which is not beneficial to process quality control and optimization. Alternatively, soft sensing methods are developed to alleviate the problem. − The concerned hard-to-measure quality variables are estimated with the help of process variables. As known, adequate training samples are a key factor in reliable model construction.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

et al. 2023

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Although recent transfer learning soft sensors show promising applications in multigrade chemical processes, good prediction performance mainly relies on available target domain data, which is difficult to achieve for a start-up grade. Additionally, only employing a single global model is inadequate to characterize the inner relationship of process variables. A just-in-time adversarial transfer learning (JATL) soft sensing method is developed to enhance multigrade process prediction performance. The distribution discrepancies of process variables between two different operating grades are first reduced by the ATL strategy. Subsequently, by applying the just-in-time learning approach, a similar data set is selected from the transferred source data for reliable model construction. Consequently, with the JATL-based soft sensor, quality prediction of a new target grade is implemented without its own labeled data. Experimental results on two multigrade chemical processes validate that the JATL method can give rise to the improvement of model performance.

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Section: Introductionmentioning

confidence: 99%

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

et al. 2023

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“…[2][3][4] In more detail, soft sensor methods can be roughly classified into a mechanistic method and a data-driven method [5][6][7] where the first type depends on accurate mechanistic knowledge, which is often not available as industrial processes have become increasingly complex. [8,9] As for the second method, it has emerged as the most popular soft sensor method in recent years. [10,11] These methods do not require reaction principles of chemical processes, but they only use historical data to establish the mathematical model to predict the key variables.…”

Section: Introductionmentioning

confidence: 99%

Multi‐layer features ensemble soft sensor regression model based on stacked autoencoder and vine copula

Chen

2023

Can J Chem Eng

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It is crucial in industrial processes to consider key variables to ensure safe operation and high product quality. Moreover, these variables are difficult to obtain using traditional measurement methods; hence, it makes sense to develop soft sensor regression models to process the variable prediction. However, there are numerous variables integrating noisy and redundant information in complex industrial processes. Using such variables in traditional regression models may result in reducing the model's efficiency and performance. Thus, this paper proposes a multi-layer feature ensemble soft sensor regression method using a stacked auto-encoder (SAE) and vine copula (ESAE-VCR) to address these problems. To do so, the number of neurons in the hidden layer of the SAE is determined by the principal component analysis (PCA). The multi-layer features of the process variables are extracted using a stacked AE, and the regression models are established for each feature layer. A linear regression ensemble method is used to combine the regression models with the multi-layer features to obtain the final predictive model that will estimate the values of the key variables. The effectiveness and practicality of the ESAE-VCR are validated by comparing them with several common soft measurement methods in two examples. In the numerical example, the ESAE-VCR yields an accuracy of prediction (R 2 ) of 0.9898 and a root mean square error (RMSE) of 0.1804. In the industrial example, the ESAE-VCR yields an R 2 of 0.9908 and an RMSE of 0.1205.

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“…Its wide ranges of applications include performance prediction, state estimation, real-time control, performance optimization, fault estimation, etc. [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Fault-tolerant Soft Sensors for Dynamic Systems

Chen¹,

Huang²

2022

Preprint

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Unpredicted faults occurring in automation systems deteriorate the performance of soft sensors and may even lead to incorrect results. In order to address the problem, this study develops three novel data-driven approaches for development of soft sensors. The three proposed soft sensors have fault-tolerant abilities. They are respectively called measurement space-aided scheme (MSaS), subspace-aided scheme (SSaS), and improved MSaS (IMSaS). As means to obtain more accurate results of soft sensors in the online phase, 1) MSaS constructs an optimal estimator of faults in the measurement space; 2) SSaS removes the influences caused by unknown sensor faults with the aid of a constructed subspace; 3) IMSaS is an improved version of MSaS, eliminating the influences of the past prediction error that may accumulate and affect the current prediction result. They are the output-driven fault-tolerant soft sensors because their implementations rely on system measurements only. Furthermore, performance analysis is also conducted to investigate the estimation errors. Both the sufficient and necessary conditions for these designs are provided, and illustrations of the effectiveness and feasibility of the three proposed fault-tolerant soft sensors based on two case studies are given.

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Synthesizing labeled data to enhance soft sensor performance in data-scarce regions

Cited by 14 publications

References 44 publications

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

Multi‐layer features ensemble soft sensor regression model based on stacked autoencoder and vine copula

Fault-tolerant Soft Sensors for Dynamic Systems

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