Novel Approaches in Fault Detection of Electrical Equipment Using Multiple Monitoring Signals
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 30 October 2024 | Viewed by 248
Special Issue Editor
2. Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA
Interests: intelligent condition monitoring; reliability modeling and estimation; fault diagnostics and prognostics; intelligent operation and maintenance
Special Issue Information
Dear Colleagues,
Fault detection plays a significant role in modern industrial production by enabling the early identification of equipment faults, damages, or failures, as well as the timely implementation of maintenance measures to prevent operational disruptions, reduce maintenance costs, and avoid potential safety hazards. However, the complexity of electrical systems in practical engineering poses major challenges to fault detection. These systems often consist of multiple components with various dependencies and failure modes, and they may contain multiple types of degradation information that reflect their health status, further complicating the fault detection process. Therefore, there is a pressing need to develop new methods for accurately detecting faults in industrial electrical equipment by effectively utilizing multiple sensor signals. Despite the fact that numerous studies on fault detection research have been published in various journals and academic forums, some key issues concerning the use of multiple sensor signals remain unexplored and unaddressed. To address this, this Special Issue welcomes original research papers that have been experimentally validated, as well as review articles and technical assessment reports.
The main topics of this Special Issue include, but are not limited to, the following:
- Fault mechanisms of modern electrical equipment;
- Signal processing for multiple monitoring signals;
- Degradation modeling of modern electrical equipment;
- Health status assessment under multiple monitoring signals;
- Remaining useful life prediction for fault detection of electrical equipment;
- Fault detection using artificial intelligence and digital twin techniques;
- Instruments for fault detection of electrical equipment;
- Intelligent maintenance for electrical equipment considering fault dependency.
Dr. Chaoqun Duan
Guest Editor
Manuscript Submission Information
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Keywords
- condition monitoring
- fault detection
- degradation modeling
- fault prognosis
- health assessment
- intelligent maintenance
- multiple monitoring signals
