Fault Diagnosis and Fault Tolerant Control in Mechanical System
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 116
Special Issue Editor
Special Issue Information
Dear Colleagues,
Fault diagnosis and fault-tolerant control (FTC) technologies are pivotal for ensuring the safety, reliability, and operational continuity of modern mechanical systems under performance degradation or component failures. With the advent of Industry 4.0, these fields are experiencing revolutionary transformations through the integration of advanced sensing, artificial intelligence, and cyber–physical system architectures. The synergy of data-driven diagnostics and resilient control strategies enables mechanical systems to autonomously detect incipient faults, reconfigure control actions, and maintain operational integrity in critical applications—from aerospace propulsion to robotic manufacturing. This Special Issue aims to showcase pioneering research and practical innovations in fault diagnosis and FTC for mechanical systems. We invite contributions addressing the latest methodologies, theoretical breakthroughs, and industrial implementations. Topics of interest include, but are not limited to, the following: AI-enhanced fault identification: deep transfer learning, few-shot learning, and physics-informed neural networks for limited data scenarios. Resilient control architectures: self-healing control, adaptive sliding-mode FTC, and distributed FTC for multi-agent systems. Digital twin-enabled solutions: real-time virtual replicas for fault simulation, prognosis, and control reconfiguration. Cross-domain fusion techniques: multi-sensor fusion (vibration, thermal, acoustic) and heterogeneous data integration under variable operating conditions.
Dr. Zhen Jia
Guest Editor
Manuscript Submission Information
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Keywords
- fault diagnosis
- fault-tolerant control
- digital twins
- physics-informed neural networks
- multi-sensor fusion
- cyber–physical systems
- resilient control transfer learning
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