IEEE Trans Cybern. 2022 May;52(5):3457-3468. doi: 10.1109/TCYB.2020.3010331. Epub 2022 May 19.
These days, data-driven soft sensors have been widely applied to estimate the difficult-to-measure quality variables in the industrial process. How to extract effective feature representations from complex process data is still the difficult and hot spot in the soft sensing application field. Deep learning (DL), which has made great progresses in many fields recently, has been used for process monitoring and quality prediction purposes for its outstanding nonlinear modeling and feature extraction abilities. In this work, deep stacked autoencoder (SAE) is introduced to construct a soft sensor model. Nevertheless, conventional SAE-based methods do not take information related to target values in the pretraining stage and just use the feature representations in the last hidden layer for final prediction. To this end, a novel gated stacked target-related autoencoder (GSTAE) is proposed for improving modeling performance in view of the above two issues. By adding prediction errors of target values into the loss function when executing a layerwise pretraining procedure, the target-related information is used to guide the feature learning process. Besides, gated neurons are utilized to control the information flow from different layers to the final output neuron that take full advantage of different levels of abstraction representations and quantify their contributions. Finally, the effectiveness and feasibility of the proposed approach are verified in two real industrial cases.
如今，数据驱动的软传感器已广泛应用于估计工业过程中难以测量的质量变量。如何从复杂的过程数据中提取有效的特征表示仍然是软传感应用领域的难点和热点。深度学习（DL）最近在许多领域取得了重大进展，由于其出色的非线性建模和特征提取能力，已被用于过程监测和质量预测目的。在这项工作中，引入了深度堆叠自动编码器（SAE）来构建软传感器模型。然而，传统的基于 SAE 的方法在预训练阶段不考虑与目标值相关的信息，而只是使用最后隐藏层中的特征表示进行最终预测。为此，针对上述两个问题，提出了一种新的门控堆叠目标相关自动编码器（GSTAE），以提高建模性能。通过在逐层预训练过程中添加目标值的预测误差到损失函数中，使用目标相关信息来指导特征学习过程。此外，利用门控神经元来控制来自不同层的信息流到最终输出神经元，从而充分利用不同层次的抽象表示，并量化它们的贡献。最后，在两个实际的工业案例中验证了所提出方法的有效性和可行性。
