Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition

Dear Colleagues,

In the current era, the rapid progress of artificial intelligence (AI) has led to the implementation of various AI techniques to ensure equipment and production reliability, safety, and quality, as well as to prevent unexpected failures within smart manufacturing systems. The widespread application of AI techniques presents new opportunities in the realm of smart manufacturing, particularly in the domains of intelligent fault diagnosis, prognosis, and surface defect detection. These AI-supported approaches are proficient in analyzing industrial signals or images to monitor the health and functionality of machines or products, showcasing significant potential to enhance the safety and efficiency of smart manufacturing practices. The proposed Special Issue on artificial-intelligence-driven intelligent fault prediction and health management techniques in manufacturing systems is dedicated to exploring the theories, methodologies, and practical applications of AI techniques within smart manufacturing environments. Researchers are encouraged to leverage various industrial data sources, such as signals, images, or videos, to diagnose and predict the operational status of machines and products.

This Special Issue aims to explore innovative applications of AI in the domains of intelligent fault diagnosis, prognosis, and surface defect detection. We invite contributions that delve into the theoretical foundations, methodological frameworks, and practical implementations of AI-driven techniques in manufacturing systems.

Topics of interest include but are not limited to the following:

• AI applications in intelligent fault diagnosis and prediction;
• AI-supported industrial signal and image analysis;
• AI-driven machine and product health management methods;
• AI-driven fault prediction and health management techniques in smart manufacturing systems;
• Industrial big data analytics and AI fusion in smart manufacturing systems.

Prof. Dr. Long Wen
Dr. Zhuyun Chen
Prof. Dr. Chuanjiang Li
Dr. Junyu Qi
Prof. Dr. Gernot Schullerus
Prof. Dr. Jianan Wei
Guest Editors

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