Yang Zeyu, Jia Ruining, Wang Peiliang, Yao Le, Shen Bingbing
Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems, School of Engineering, Huzhou University, Huzhou 313000, China.
School of Mathematics, Hangzhou Normal University, Hangzhou 311121, China.
ACS Omega. 2023 Jan 18;8(4):4196-4208. doi: 10.1021/acsomega.2c07400. eCollection 2023 Jan 31.
Soft sensors are mathematical methods that describe the dependence of primary variables on secondary variables. A nonlinear characteristic commonly appears in modern industrial process data with increasing complexity and dynamics, which has brought challenges to soft sensor modeling. To solve these issues, a novel supervised attention-based bidirectional long short-term memory (SA-BiLSTM) is first proposed in this paper to handle the nonlinear industrial process modeling with dynamic features. In this SA-BiLSTM model, an attention mechanism is introduced to calculate the correlation between hidden features in each time step, thus avoiding the loss of important information. Furthermore, this approach combines historical quality information and a moving window through a supervised strategy of quality variables. Such manipulation not only extracts and exploits nonlinear dynamic latent information from the process and quality variables but also enhances the model's learning efficiency and overall prediction performance. Finally, two real industrial examples demonstrate the superiority of the proposed method compared to conventional methods.
软传感器是描述主要变量与次要变量之间依赖关系的数学方法。随着现代工业过程数据的复杂性和动态性不断增加，非线性特性普遍出现，这给软传感器建模带来了挑战。为了解决这些问题，本文首先提出了一种新颖的基于监督注意力的双向长短期记忆网络（SA-BiLSTM），以处理具有动态特征的非线性工业过程建模。在这个SA-BiLSTM模型中，引入了一种注意力机制来计算每个时间步隐藏特征之间的相关性，从而避免重要信息的丢失。此外，该方法通过质量变量的监督策略将历史质量信息和移动窗口相结合。这种操作不仅从过程和质量变量中提取并利用了非线性动态潜在信息，还提高了模型的学习效率和整体预测性能。最后，两个实际工业实例证明了所提方法相对于传统方法的优越性。
