Recent Advances in Data-Driven Virtual Sensing Technology for Smart Industries

Dear Colleagues,

With the rising trends in digitalization, enormous amounts of data are being generated in modern industries. The availability and increasing quantity of data have attracted great attention of researchers in the industrial artificial intelligence. Data-driven virtual sensing or soft sensing technologies aim to use artificial intelligence methodologies in predicting KPIs or quality variables that  cannot be automatically measured in general, or can only be measured sporadically with high delays or at a high cost. However, challenges still exist in the development of data-driven virtual sensing. For example, how to effectively use the existing huge amount of knowledge to improve virtual-sensing systems; how to analyze and process big data in a more effective and cost-reducing way; how to discover and interpret knowledge from the data; how to generalize and transfer discoveries into other application fields; how to build reliable virtual-sensing systems using limited labeled data; etc.

This Special Issue aims to provide a forum for researchers and practitioners to present innovative solutions for data-driven virtual-sensing systems, and to identify new challenges and future research directions in related fields. The topics of interest for this Special Issue include, but are not limited to:

• Applications of data-driven virtual sensors to industrial processes;
• Data-driven virtual sensors based on big data analytics;
• Data-driven virtual sensors based on statistical machine learning;
• Data-driven virtual sensors based on deep learning;
• Data-driven virtual sensors based on transfer learning;
• Data knowledge integration-driven virtual sensors;
• Understanding, visualizing and interpreting learning models;
• New theories and architectures for data-driven virtual sensors.

Dr. Xinmin Zhang
Dr. Weiming Shao
Dr. Tao Peng
Guest Editors

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• smart technologies
• smart industries
• industrial big data
• virtual sensor
• soft sensor
• process control and monitoring
• industrial intelligence
• fault classification and diagnosis in industries