Stacked maximal quality-driven autoencoder: Deep feature representation for soft analyzer and its application on industrial processes

Abstract

Deep learning based soft analyzers are important for modern industrial process monitoring and measurement, which aim to establish prediction models between quality data and easy-to-measure variables. However, in traditional deep learning methods, the guidance of quality information on feature extraction is insufficient and easily reduces as data dimension increases. In this paper, a stacked maximal quality-driven autoencoder (SMQAE) is proposed to extract maximal quality-relevant features for soft analyzers. In each maximal quality-driven autoencoder, quality variables are reconstructed together with the input variables in the output layer. The SMQAE ensures that the influence of the quality part and input part on the reconstruction are the same. And the maximal information coefficient (MIC), which is not limited to any specific function type, is exploited to enhance the importance of quality-related variables in the input part. With the constraint of the quality equivalence strategy and variable importance evaluation based on MIC, the SMQAE maximizes the guidance of the quality variables during feature learning without the interference of the data dimensions. Therefore, the SMQAE can extract quality-relevant features for complex high-dimensional data. The rationality, superiority and robustness of SMQAE based soft analyzers are validated on four simulated scenarios and two industrial processes.