Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 87
Special Issue Editors
Interests: deep learning; automatic machine learning; fault diagnosis; intelligent algorithm
Special Issues, Collections and Topics in MDPI journals
Interests: deep transfer learning; federated learning; signal processing; fault diagnosis
Interests: Intelligent PHM; few-shot fault diagnosis; UAVs data analysis; meta-learning.
Interests: fault detection of wind turbine gearboxes
Interests: energy efficient control of induction machines, optimal control of electrical drives, and condition monitoring
Interests: manufacturing big data and manufacturing information systems; intelligent manufacturing; machine learning; deep transfer learning; fault diagnosis; imbalanced data processing and predictive maintenance
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Prof. Dr. Chuanjiang Li
Dr. Junyu Qi
Dr. Zhuyun Chen
Prof. Dr. Gernot Schullerus
Prof. Dr. Jianan Wei
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- fault prediction
- defect detection
- artificial intelligence
- smart manufacturing
