Artificial Intelligence in Fault Diagnosis and Signal Processing, 2nd Edition
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 January 2026 | Viewed by 19
Special Issue Editors
Interests: condition monitoring; fault detection; artificial intelligence; deep learning; signal processing; electromechanical systems
Special Issues, Collections and Topics in MDPI journals
Interests: electric power systems; artificial intelligence; optimization algorithms; condition monitoring; power quality
Special Issue Information
Dear Colleagues,
The early detection and diagnosis of faults is essential in industrial processes since it can help avoid potentially irreparable damage to machinery, which could reduce the performance of the control system and process efficiency, ultimately resulting in a decrease in production. Additionally, in terms of industrial safety, timely fault detection and diagnosis can facilitate safer operations, reducing the risks to which plant workers are exposed. Therefore, detecting and diagnosing faults quickly and accurately can facilitate decision-making in a way that enables corrective actions to be taken to repair damaged components. In recent years, various machine fault detection techniques have emerged, and artificial intelligence and signal processing have become essential components thereof. However, this research field continues to generate new trends in terms of the methodologies related to multiple fault detection, novelty detection, data mining, development in hardware, etc.
The goal of this Special Issue is to bring together researchers and industrial practitioners to share their research findings and present ideas that are relevant to the field of fault diagnosis using artificial intelligence and signal processing.
Dr. Juan Jose Saucedo-Dorantes
Dr. David Alejandro Elvira-Ortiz
Guest Editors
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Keywords
- neural networks
- machine learning
- sensors
- novelty detection
- data mining
- signal processing methods
- signal processing implementation
- FPGA
- HIL
- industrial applications
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