Physical-Informed Fault Monitoring and Fault-Tolerant Control of Industrial System
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 175
Special Issue Editors
Interests: fault diagnosis and condition monitoring of electromechanical systems
Special Issues, Collections and Topics in MDPI journals
Interests: fault-tolerant control; adaptive control and identification of network systems
Special Issue Information
Dear Colleagues,
Owing to the rapid development of Internet of Things (IOT) and sensors networks in industrial enginnering, prognostic and health management (PHM) schemes have been widely adopted in next generation of industrial systems such as aircraft engines, energies, high speed train, vehicles, and other heavy industries areas. In particular, physics-informed fault monitoring and fault-tolerant control has emerged as a promising technique for condition monitoring of complex industrial system, which combines the superiorities of degradation mechanism, models interpretability and performance reliability even in the event of system failure through fusing domain knowledge and degraded information of system. This special issue aims at addressing and reporting the recent advances and new breakthrough in physical-informed fault monitoring and tolerance control of industrial system. Authors are encouraged to submit research papers or practical applications. Areas to be covered in this Research Topic may include, but are not limited to:
- Theoretical modeling for fault monitoring & tolerance control
- Physics-informed deep learning for fault diagnosis & classification
- Physics-informed deep learning for failure prognosis & life-time prediction
- Active fault-tolerant control of industrial system
- Passive fault-tolerant control of industrial system
- Implementation of physics-informed modeling in embedded environment for IoT&PHM applications
- Self-healing, evaluation, validation & invitigation of physics-informed models/algorithms
- Fault compensation, redundant decision and adaptive control to industrial systems
- Applications of physical-informed fault monitoring to various industrial systems/domains
- Uncertainty and stability assessment in physical-informed models
Dr. Qing Li
Dr. Yu Shao
Dr. Xiang Chen
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. Machines is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- theoretical modeling for fault monitoring and tolerance control
- physics-informed deep learning for fault diagnosis and classification
- physics-informed deep learning for failure prognosis and life-time prediction
- active fault-tolerant control of industrial system
- passive fault-tolerant control of industrial system
- implementation of physics-informed modeling in embedded environment for IoT and PHM applications
- self-healing, evaluation, validation, and investigation of physics-informed models/algorithms
- fault compensation, redundant decision, and adaptive control in industrial systems
- applications of physical-informed fault monitoring in various industrial systems/domains
- uncertainty and stability assessments in physical-informed models
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