Submission Deadline: 30 September 2026 View: 81 Submit to Special Issue
Prof. Quan Qian
Email: qian_1998@uestc.edu.cn
Affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Research Interests: transfer learning, process control, intelligent fault diagnosis and RUL prediction
Prof. Hanmin Sheng
Email: hmsheng@uestc.edu.cn
Affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Research Interests: artificial intelligence and detection technology
Prof. Jiusi Zhang
Email: jiusi.zhang@uestc.edu.cn
Affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Research Interests: intelligent fault diagnosis/prognosis/tolerance, industrial big data and artificial intelligence, data-driven monitoring and optimization, intelligent operation and maintenance of complex industrial systems
Prof. Sheng Xiang
Email: xiangsheng@cqupt.edu.cn
Affiliation: School of Automation Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
Research Interests: RUL prediction, large language model, edge and efficient AI, multidimensional time series
With the increasing scale and operational complexity of modern industrial systems, ensuring reliability and safety has become a critical challenge. Equipment such as rotating machinery and energy systems often operate under harsh and time-varying conditions, where nonlinearity and uncertainty complicate condition monitoring and maintenance. Fault diagnosis and prognostics are therefore essential for condition-based maintenance and performance optimization.
Recent advances in sensing technologies and computational methods have enabled data-driven and model-based health assessment approaches. However, practical applications still face challenges arising from complex operating conditions, limited fault data, distribution shifts, and poor generalization. Addressing these issues requires robust computational frameworks that integrate signal analysis, intelligent learning, and degradation modeling.
This Special Issue aims to present recent theoretical advances, computational methods, and practical applications in fault diagnosis and prognostics for complex industrial systems, with an emphasis on real-world engineering scenarios. This Special Issue welcomes original research articles, methodological studies, and application-oriented contributions. Suggested themes include, but are not limited to:
· Intelligent fault diagnosis methods
· Remaining useful life prediction
· Advanced signal processing and feature extraction for condition monitoring
· Domain adaptation and domain generalization for cross-condition or cross-system diagnosis
· Health indicator construction and degradation modeling
· Data-driven and hybrid modeling approaches
· Robust diagnosis under nonstationary and time-varying operating conditions
· Multi-sensor data fusion and representation learning
· Industrial case studies and real-world applications of fault diagnosis and prognostics
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