Advances in sensor and data storage technologies have enabled the cumulation of a large amount of data from industrial systems. Both structural and nonstructural data of industrial systems are collected, which covers data formats of time-series, text, images, sound, etc.

Since data can systematically describe the status of industrial systems, their utilization has been attending growing interest from the industry and data intelligence methods are highly desired. Meanwhile, the theoretical development in related disciplines, such as machine learning, computer vision, evolutionary computation, and signal processing, has provided effective ways of analyzing and utilizing the collected data. The recent success of applying data-driven methods in different domains, including intelligent manufacturing, energy, internet, and smart healthcare, has proved the potential of employing data intelligence algorithms for solving real problems in various industrial fields.

This Special Issue aims to provide a forum for researchers to present the latest research on the applications of data intelligence algorithms in industrial systems, especially considering data fusion approaches of integrating different data sources and fusing structural and nonstructural information. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Detection, tracking, location, and classification under industrial environment
• Machine learning and deep learning for intelligent manufacturing
• Metaheuristic algorithms for facility layout optimization
• Multi-source data fusion for industrial systems
• Smart data analytics in industrial product design
• Mobile computing and sensing for industrial systems