Advanced Fault Diagnosis Using Interpretable, Multimodal and Transfer Learning Techniques
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 51
Special Issue Editors
Interests: artificial intelligence; signal processing; fault diagnosis; condition based monitoring
Special Issue Information
Dear Colleagues,
Modern industrial systems require reliable monitoring to prevent failures and support safe operation. Traditional diagnostic techniques often lose effectiveness when signals become complex, noisy, or originate from different sensing sources. With the growth of machine learning, multimodal data fusion and transfer learning have become powerful strategies to improve generalisation and performance, yet many existing approaches still function like closed black boxes and remain difficult to interpret or deploy in practice.
This Special Issue focuses on the next generation of fault diagnosis methods that enhance interpretability, domain awareness, and adaptability across different machines and environments. We welcome studies that explore clear and explainable models, physics guided learning, multimodal fusion of vibration, acoustic emission, thermal or current signals, and transfer learning strategies that support cross domain or cross system generalisation. Research that demonstrates real world validation, industrial case studies, or real time deployment on edge devices is strongly encouraged.
This Special Issue aims to bring forward contributions that combine methodological innovation with practical value for fault diagnosis in modern industrial systems.
Dr. Muhammad Farooq Siddique
Dr. Izaz Raouf
Guest Editors
Manuscript Submission Information
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Keywords
- interpretable machine learning
- multimodal learning
- transfer learning
- physics guided models
- sensor fusion
- condition monitoring
- intelligent diagnostic methods
- industrial artificial intelligence
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