Guest Editors
Prof. Haidong Shao, Hunan University, China
Dr. Jipu Li, The Hong Kong Polytechnic University, China
Prof. Yun Kong, Beijing Institute of Technology, China
Prof. Zhuyun Chen, South China University of Technology, China
Summary
In recent years, the rapid development of industrial big data and artificial intelligence (AI) technologies has revolutionized the industrial landscape. Industrial systems, such as manufacturing, energy, transportation, and logistics, have become increasingly complex, generating vast amounts of data. These big data encompass a wide range of data sources, including sensor data, production logs, and maintenance records, which hold valuable insights. Moreover, machine learning-based AI techniques can be applied to extract meaningful insights from this big data. For example, deep learning allows machines to interpret and understand multisensory information, which can be utilized for quality control, defect detection, and object recognition in industrial systems. Transfer learning can improve predictive maintenance models, anomaly detection, and fault diagnosis by transferring knowledge learned from similar systems. Reinforcement learning empowers machines to learn from trial and error, making it suitable for optimization problems in industrial systems. As such, the integration of industrial big data and AI enables intelligent perception, maintenance, and decision optimization, driving the intelligent upgrade of enterprises and enhancing productivity and quality.
Keywords
This special issue aims to bring together researchers and practitioners from academia and industry to explore the latest advancements in industrial big data and AI-driven intelligent perception, operation, and decision optimization in industrial systems. We invite original research papers, case studies, and review articles that address the following topics (but not limited to):
Deep learning-based industrial big data acquisition, storage, and preprocessing techniques.
Data analytics information fusion, pattern recognition, and trend analysis for industrial systems with advanced machine learning methods.
Deep transfer learning and meta learning for intelligent maintenance including abnormal detection, fault diagnosis, performance prediction.
Federated learning for data privacy and collaborative training of model.
Reinforcement learning for decision optimization, including supply chain management, demand forecasting, and quality assurance.
Explainable machine learning for intelligent maintenance and decision optimization.
Digital-twin technique for decision optimization and performance management.
Published Papers
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