Semisupervised dynamic soft sensor based on complementary ensemble empirical mode decomposition and deep learning

Abstract: As an effective means of sensing difficult-to-measure process variables in real time, soft sensors are widely used but have a few significant limitations. Modeling errors between the mechanism model and real system can occur, which affect the accuracy of the mechanism-driven sensing result. Furthermore, deep learning-based data-driven soft sensors are complex black boxes, resulting in a lack of interpretability in terms of the established model and a lack of reliability regarding the sensing results. To solve these problems, this paper introduces the generative adversarial network (GAN) for soft sensor modeling and establishes an innovative GAN-based hybrid mechanism- and data-driven soft sensor framework. Meanwhile, considering the dynamic characteristics of the industrial process, a deep gated recurrent unit (DGRU) was adopted to compensate for the modeling errors in the mechanism model. This deep learning-based data-driven model not only captures the timing relationship between sensor data but also uses numerous unlabeled data. The generator to be identified in the GAN consists of the DGRU and mechanism model. In an industrial case for predicting the rotor thermal deformation of an air preheater in a power station boiler, the effectiveness and superiority of the hybrid-driven dynamic soft sensor model were verified, and the post hoc interpretability of the model was explained by manipulating the latent variables.

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.