Details

Autor(en) / Beteiligte

• Ou, Chen
• Zhu, Hongqiu
• Shardt, Yuri A. W.
• Ye, Lingjian
• Yuan, Xiaofeng
• Wang, Yalin
• Yang, Chunhua

Titel

Quality-Driven Regularization for Deep Learning Networks and Its Application to Industrial Soft Sensors

Ist Teil von

• IEEE transaction on neural networks and learning systems, 2022-02, Vol.PP, p.1-11

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2022

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• The growth of data collection in industrial processes has led to a renewed emphasis on the development of data-driven soft sensors. A key step in building an accurate, reliable soft sensor is feature representation. Deep networks have shown great ability to learn hierarchical data features using unsupervised pretraining and supervised fine-tuning. For typical deep networks like stacked auto-encoder (SAE), the pretraining stage is unsupervised, in which some important information related to quality variables may be discarded. In this article, a new quality-driven regularization (QR) is proposed for deep networks to learn quality-related features from industrial process data. Specifically, a QR-based SAE (QR-SAE) is developed, which changes the loss function to control the weights of the different input variables. By choosing an appropriate inductive bias for the weight matrix, the model provides quality-relevant information for predictive modeling. Finally, the proposed QR-SAE is used to predict the quality of a real industrial hydrocracking process. Comparative experiments show that QR-SAE can extract quality-related features and achieve accurate prediction performance.