Intelligent Manufacturing and Medical-Engineering Integration

Dear Colleagues,

This Special Issue aims to explore research and applications in cutting-edge fields such as Manufacturing Big Data and Information Systems, Intelligent Manufacturing and Maintenance, and Medical-Engineering Integration Manufacturing. With the intelligent transformation of the manufacturing industry, big data technology is playing an increasingly important role in production processes. This Special Issue will focus on how to apply advanced technologies like big data analytics, machine learning, and deep learning to achieve process optimization, fault prediction and maintenance, and resource utilization maximization in manufacturing. Additionally, this Special Issue will also address the cross-disciplinary domains of intelligent manufacturing and medical-engineering integration, investigating the application of intelligent manufacturing technologies in the manufacturing and operation of medical devices to enhance the efficiency and quality of the medical industry. Scholars from academia and industry are welcome to participate and share the latest research findings and innovative ideas in the fields of intelligent manufacturing and medical-engineering integration.

Potential topics include, but are not limited to:

• Intelligent fault prediction and prevention driven by big data: Utilizing big data analysis and machine learning to achieve early prediction and prevention of equipment failures, optimizing production processes, and reducing maintenance costs.
• Intelligent fault diagnosis models and algorithms: Researching algorithms like imbalanced learning, few-shot learning, positive-unlabeled learning (PU learning), zero-shot learning, and modeling methods under various operating conditions to provide efficient and accurate solutions for mechanical equipment fault diagnosis.
• Cross-disciplinary innovation in medical-engineering integration manufacturing: Applying intelligent manufacturing technology to the manufacturing and operation of medical devices to improve the efficiency and quality of the medical industry.
• Advancements in fault diagnosis/prediction and remaining useful life estimation using deep learning algorithms: Exploring the application of deep learning in data analysis and remaining useful life prediction for achieving more accurate predictive maintenance.
• Data-driven medical diagnosis: Utilizing deep learning and data analysis techniques for intelligent diagnosis of medical images and patient data to enhance the accuracy and efficiency of medical diagnosis.
• Construction and optimization of manufacturing information systems: Building intelligent manufacturing information systems and incorporating technologies like imbalanced data processing, time series data analysis, and natural language processing to optimize manufacturing processes, improving efficiency and quality in manufacturing.

Prof. Dr. Haisong Huang
Dr. Jianan Wei
Prof. Dr. Long Wen
Dr. Zhuyun Chen
Guest Editors

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