A novel soft sensor approach for industrial quality prediction based TCN with spatial and temporal attention

IF 3.7 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS
Lei Zhang , Guofeng Ren , Shanlian Li , Jinsong Du , Dayong Xu , Yinhua Li
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引用次数: 0

Abstract

The complex industrial process is often characterized by strong multivariate coupling and nonlinear dynamic changes, which pose great challenges to modeling and prediction. Traditional deep learning methods are difficult to effectively capture spatiotemporal characteristics of industrial processes, resulting in poor prediction accuracy. To tackle this issue, we propose a novel end-to-end method named STA-TCN, which utilizes a temporal convolutional network (TCN) with both spatial and temporal attention mechanisms. The TCN uses causal and dilated convolutions to capture long temporal patterns in time series data. The spatial attention identifies the significance of different features, while the temporal attention focuses on crucial time steps. This design assigns adaptive weights to different features and emphasizes key moments to improve the accuracy of dynamic processes. We conduct experiments on two industrial datasets and show that the proposed STA-TCN method achieves significantly improved predictive performance compared to TCN for quality prediction of industrial processes. The results validate the effectiveness and robustness of the proposed method.
基于时空关注的TCN工业质量预测软测量方法
复杂的工业过程往往具有强多元耦合和非线性动态变化的特点,这对建模和预测提出了很大的挑战。传统的深度学习方法难以有效捕捉工业过程的时空特征,导致预测精度较差。为了解决这个问题,我们提出了一种新的端到端方法,称为STA-TCN,它利用具有空间和时间注意机制的时间卷积网络(TCN)。TCN使用因果和扩展卷积来捕获时间序列数据中的长时间模式。空间注意识别不同特征的重要性,而时间注意则关注关键的时间步骤。该设计为不同的特征赋予自适应权重,并强调关键时刻,以提高动态过程的精度。我们在两个工业数据集上进行了实验,结果表明,与TCN相比,本文提出的STA-TCN方法在工业过程质量预测方面取得了显著提高的预测性能。实验结果验证了该方法的有效性和鲁棒性。
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来源期刊
CiteScore
7.50
自引率
7.70%
发文量
169
审稿时长
3.4 months
期刊介绍: Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.
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