AI-Enabled Prognostics and Health Management: Mathematical Aspect and Engineering Applications
A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E2: Control Theory and Mechanics".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 755
Editors
Interests: intelligent maintenance; digital twin technology for complex equipment; full life cycle management of civil aviation engines
Interests: data imbalance; over-sampling; out-of-distribution samples; fault detection
Special Issue Information
Dear Colleagues,
This Special Issue of Mathematics focuses on the intersection of artificial intelligence and prognostics and health management (PHM), with a specific emphasis on underlying mathematical frameworks and their real-world engineering applications. As modern engineering systems grow in complexity, the ability to accurately predict remaining useful life and detect incipient faults is critical for ensuring safety and reliability. This collection invites contributions that explore advanced AI methodologies—such as deep learning, statistical modeling, and optimization algorithms—to address core PHM challenges, including handling data imbalance, out-of-distribution samples, and uncertainty quantification. The goal is to showcase cutting-edge research that bridges the gap between theoretical mathematical models and practical deployment in fields like aeronautics and mechatronics. We welcome original research articles and review papers that push the boundaries of data-driven fault detection and diagnostics, fostering collaboration between mathematicians and engineers to develop robust, intelligent health management solutions.
Prof. Dr. Lin Lin
Dr. Dan Liu
Guest Editors
Dr. Zhiqi Yan
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- prognostics and health management (PHM)
- remaining useful life (RUL) prediction
- fault detection and diagnosis
- condition-based maintenance
- predictive maintenance
- machine learning/deep learning
- statistical learning theory
- optimization algorithms
- uncertainty quantification
- signal processing
- data imbalance
- out-of-distribution (OOD) detection
- over-sampling techniques
- anomaly detection
- aeronautics/aerospace engineering
- mechatronics
- industrial systems
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