IEEE Trans Neural Netw Learn Syst. 2021 Aug;32(8):3306-3317. doi: 10.1109/TNNLS.2020.3015929. Epub 2021 Aug 3.
Soft sensor techniques have been applied to predict the hard-to-measure quality variables based on the easy-to-measure process variables in industry scenarios. Since the products are usually produced with prearranged processing orders, the sequential dependence among different variables can be important for the process modeling. To use this property, a dual attention-based encoder-decoder is developed in this article, which presents a customized sequence-to-sequence learning for soft sensor. We reveal that different quality variables in the same process are sequentially dependent on each other and the process variables are natural time sequences. Hence, the encoder-decoder is constructed to explicitly exploit the sequential information of both the input, that is, the process variables, and the output, that is, the quality variables. The encoder and decoder modules are specified as the long short-term memory network. In addition, since different process variables and time points impose different effects on the quality variables, a dual attention mechanism is embedded into the encoder-decoder to concurrently search the quality-related process variables and time points for a fine-grained quality prediction. Comprehensive experiments are performed based on a real cigarette production process and a benchmark multiphase flow process, which illustrate the effectiveness of the proposed encoder-decoder and its sequence to sequence learning for soft sensor.
软测量技术已被应用于基于易于测量的过程变量来预测难以测量的质量变量，因为产品通常是按照预定的加工顺序生产的，所以不同变量之间的顺序依赖关系对于过程建模很重要。为了利用这一特性，本文提出了一种基于双注意力的编解码器，为软测量提供了一种定制的序列到序列学习方法。我们揭示了同一过程中的不同质量变量彼此之间是顺序相关的，而过程变量是自然的时间序列。因此，编解码器被构建为明确利用输入（即过程变量）和输出（即质量变量）的顺序信息。编码器和解码器模块被指定为长短时记忆网络。此外，由于不同的过程变量和时间点对质量变量有不同的影响，所以在编码器-解码器中嵌入了一种双注意力机制，以同时搜索与质量相关的过程变量和时间点，以进行细粒度的质量预测。基于真实的香烟生产过程和一个基准多相流过程进行了综合实验，结果表明，所提出的编码器-解码器及其用于软测量的序列到序列学习是有效的。
