Fault Diagnosis and Health Management Based on Artificial Intelligence
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 1107
Special Issue Editors
Interests: PHM; CBM; fault diagnosis based on AI
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis and health management
Special Issues, Collections and Topics in MDPI journals
Interests: predictive maintenance; virtual maintenance
Interests: intelligent diagnostics; prognostics and health management (PHM) for electromechanical and hydraulic systems; artificial intelligence and signal processing; digital twins and intelligent robotics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Accurately assessing health status and identifying failure modes as early as possible are key to the continuous and reliable operation of products, as well as representing the key technology of prognosis and health management (PHM). With the continuous development of big data technology, product fault diagnosis and health management based on artificial intelligence have become current research hotspots and frontiers. However, the complex and harsh operating conditions result in such advances facing the model simplicity problem of difficult hyperparameter setting and tuning, as well as the data complexity problem of insufficient labeled data, imbalanced data distribution and low signal quality, which restrict the effective application of AI-based fault diagnosis and health management. Therefore, this Special Issue focuses on these challenges and welcomes research from global scholars proposing effective solutions aimed at revealing the nonlinear mapping laws between health characterization data and fault space in multiple scenarios to provide key technological support for product condition-based maintenance (CBM). Topics of interest include, but are not limited to, the following:
- Industrial operation data analysis and processing methods;
- Convenient fault diagnosis models with simpler hyperparameter settings;
- Intelligent fault diagnosis methods with small training samples;
- Transfer fault diagnosis methods with imbalanced data distribution;
- Enhanced fault diagnosis methods with low signal quality;
- Early fault and remaining useful life prediction methods;
- Health status evaluation methods based on multi-source data;
- Maintainability design and analysis based on artificial intelligence.
Dr. Jiayu Chen
Prof. Dr. Jian Ma
Prof. Dr. Dong Zhou
Dr. Xiaoli Zhao
Guest Editors
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Keywords
- intelligent diagnosis
- health evaluation
- PHM
- CBM
- transfer diagnosis
- enhanced diagnosis
- multi-source data fusion
- small training sample
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