Industrial Soft-Sensing Technology Based on Data-Driven and Artificial Intelligence Technologies
A special issue of Sensors (ISSN 1424-8220).
Deadline for manuscript submissions: 30 November 2025 | Viewed by 132
Special Issue Editors
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: power system stability analysis and control; time-delay system; robust theory and application
Special Issues, Collections and Topics in MDPI journals
Interests: underdrive system control; intelligent control
Special Issues, Collections and Topics in MDPI journals
Interests: multi-agent cooperative control; high-precision control of electromechanical systems; anti-disturbance control; modern robust control; control theory and applications; repetitive control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advent of data-driven and artificial intelligence (AI) technologies has revolutionized the landscape of industrial soft-sensing, offering unprecedented opportunities for enhancing process monitoring, control, and optimization. Traditional hard-sensing technologies often face limitations in terms of cost, installation complexity, and real-time data acquisition. In contrast, soft-sensing technologies leverage data analytics and AI algorithms to infer critical process variables, providing a cost-effective and efficient alternative. By integrating data-driven approaches and AI, industrial soft-sensing can achieve higher accuracy, adaptability, and robustness, thereby significantly improving the overall performance and reliability of industrial systems. This Special Issue aims to explore the latest advancements and applications of data-driven and AI-based soft-sensing technologies in various industrial sectors, with a focus on promoting innovative solutions that drive efficiency, sustainability, and intelligent automation.
Scope and Objectives:
The primary objective of this Special Issue is to advance the research and development of data-driven and AI-based soft-sensing technologies for industrial applications. The scope of this Special Issue covers a wide range of industries, including, but not limited to, manufacturing, process engineering, automation, and energy systems.
For this Special Issue, original research articles and reviews are welcome to be submitted. Research areas may include, but are not limited to, the following:
- Data-driven soft-sensing techniques;
- Machine learning algorithms for soft-sensing;
- Deep learning models for process variable estimation;
- Data mining and pattern recognition in industrial processes;
- Hybrid AI models for soft-sensing applications;
- Integration of soft-sensing with industrial systems;
- Real-time data integration and analytics;
- Soft-sensing in cyber–physical production systems;
- Multi-sensor data fusion for enhanced soft-sensing;
- Soft-sensing for quality control and process optimization;
- Fault detection and diagnosis using soft-sensing;
- Advanced soft-sensing technologies;
- Transfer learning and domain adaptation in soft-sensing.
Prof. Dr. Sheng Du
Prof. Dr. Li Jin
Dr. Zixin Huang
Dr. Pan Yu
Guest Editors
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Keywords
- industrial soft-sensing
- data-driven technologies
- artificial intelligence
- process monitoring and control
- machine learning
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