Defect and Fault Tolerance in Computing and Applications: Integrating Prognostic and Health Management (PHM) Approaches
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 April 2026 | Viewed by 477
Special Issue Editors
Interests: condition monitoring and fault detection; deep reinforcement learning; intelligent maintenance of complex systems; digital twin; time–frequency transform technology; large-scale models
Interests: reliability engineering; large language models; artificial intelligence; Internet of Things; blockchain
Special Issue Information
Dear Colleagues,
With the increasing complexity of industrial applications and safety-critical infrastructure, fault tolerance and failure management in computing systems are becoming increasingly important. Prognostic and Health Management (PHM) provides essential methods for fault prediction, fault diagnosis, and system health assessment, thereby achieving resilient and reliable system operation. With the rapid development of artificial intelligence, the integration of large-scale models has brought new opportunities for intelligent operations and maintenance, achieving breakthroughs in accuracy, adaptability, and decision support.
This Special Issue is dedicated to high-quality research on the theory, methods, and applications of fault tolerance and failure management in the context of PHM. We particularly encourage research that integrates advanced computing, fault-tolerant design, and large-model-driven intelligent operations and maintenance for next-generation industrial systems.
This Special Issue will publish original research papers in the following interdisciplinary areas:
- Data-driven Prognostic and Health Management (PHM);
- Fault Mechanism Analysis;
- Intelligent Fault Monitoring and Diagnosis;
- Prediction of Remaining Useful Life;
- Large-Language-Model-Driven Intelligent Operation and Maintenance;
- Digital Twins for PHM Systems;
- Applications of PHM in Aerospace, Energy, Transportation, and Manufacturing;
- Explainable Anomaly Detection Methods;
- Reliability Modeling and Assessment of Complex Systems.
Dr. Zisheng Wang
Dr. Dun Li
Dr. Huan Wang
Guest Editors
Manuscript Submission Information
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Keywords
- prognostic health management (PHM)
- intelligent operation and maintenance
- reliability engineering
- large language model
- data-driven PHM methods
- digital twin for PHM
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