Intelligent Fault Diagnosis and Prognosis Technologies for System Health Management
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 August 2026 | Viewed by 180
Special Issue Editors
Interests: fault diagnosis; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis; reinforcement learning; unsupervised learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modern industrial systems are becoming increasingly complex, interconnected, and data-rich, placing unprecedented demands on reliability, safety, and operational efficiency. Intelligent fault diagnosis and prognosis has emerged as a core enabling technology for advanced System Health Management (SHM), providing the capability to detect incipient faults, assess system health states, and predict remaining useful life under diverse and uncertain operating conditions.
This Special Issue aims to present recent advances in data-driven, model-based, and hybrid approaches for intelligent fault diagnosis and prognosis across a wide range of engineering systems. Topics of interest include, but are not limited to, machine learning and deep learning for fault feature extraction and classification, domain adaptation and transfer learning under varying operating conditions, multi-source information fusion, digital twin-assisted health management, uncertainty quantification, and real-time decision-making for maintenance and risk mitigation.
The Special Issue welcomes original research articles and high-quality review papers that contribute new theories, algorithms, and practical solutions for enhancing the accuracy, robustness, and generalization of intelligent SHM systems in real-world applications such as aerospace, manufacturing, energy, transportation, and industrial automation.
Dr. Ke Zhao
Dr. Ruixin Wang
Dr. Zhen Jia
Guest Editors
Manuscript Submission Information
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Keywords
- intelligent fault diagnosis
- prognostics and health management
- system health management
- machine learning and deep learning
- remaining useful life prediction
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