Advances in Fault Diagnosis and Anomaly Detection
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 15948
Special Issue Editors
Interests: modeling and fault monitoring of complex industrial systems; computational energy intelligence
Interests: modeling and control of complex systems; process monitoring; fault detection and diagnosis
Special Issue Information
Dear Colleagues,
Modern industrial processes and energy systems, such as chemical reaction processes and grid-scale battery storage systems, are large-scale with interconnected units that are expensive to operate and vulnerable to faults. Any incipient and component fault may propagate to the entire system, causing system shutdown or interruption and thus leading to an enormous loss of profits and services. It is of great significance to monitor the operation of systems for early and reliably detecting and diagnosing system anomalies. Therefore, fault diagnosis has witnessed phenomenal progress from both academia and industry over the last few decades for monitoring increasingly complex systems. Such advancements are largely attributed to the emerging industrial and energy systems operating data together with the rapid integration of machine learning, deep learning, and data science techniques with fault diagnosis.
The goal of this Special Issue is to aggregate latest research outcomes in the field of advanced fault diagnosis contributing to methodology advancement, algorithm development, and practical applications. Interested authors are invited to submit high-quality papers on topics including but not limited to:
- Statistical machine learning and dimension reduction for fault diagnosis and anomaly detection;
- Fault diagnosis based on big data, deep learning, and AI techniques;
- Model-based fault detection and diagnosis;
- Prognostics, predictive maintenance and remaining useful life (RUL) prediction;
- Applications to energy systems and industrial processes
Dr. Benben Jiang
Dr. Qiugang Lu
Dr. Yang Liu
Guest Editors
Manuscript Submission Information
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