Condition Monitoring of Electrical Machines Based on Models
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: 25 February 2025 | Viewed by 155
Special Issue Editors
Interests: induction motors; fault detection and diagnosis; condition monitoring; predictive maintenance; signal processing; smart grids; power quality
Special Issues, Collections and Topics in MDPI journals
Interests: digital signal processing; electric machines; condition monitoring; spectral analysis;
Special Issue Information
Dear Colleagues,
AC induction and permanent magnet machines are used in almost any sector: power generation, industry, public and private transportation, and services. Although electrical machines are robust, they can also suffer from failures with severe consequences if not detected in time. Fault detection and diagnosis systems are therefore necessary, and the earlier the fault is detected, the more helpful they will be. This will prevent the incipient failure from evolving into a catastrophic one. This is a very active field of research. Many fault detection techniques have been developed based on monitoring different physical variables of the machines, such as vibrations, electric currents, stray flow, temperature, torque, speed, etc. Assisted or automated diagnostic systems based on statistical classifiers or AI techniques are also being developed. These diagnostic systems can be data-driven, model-based, or hybrid.
This Special Issue aims to present and disseminate the most recent advances in using models and related techniques to monitor the condition of all-electric machines.
Topics of interest for publication include, but are not limited to:
- Observers;
- State estimators;
- Artificial intelligence-based models;
- Data-driven models;
- Physics-based models;
- Transfer of knowledge;
- Digital twins;
- Residual signals;
- Physics-informed data-driven models;
- Hybrid models;
- Signal-based condition monitoring.
Prof. Dr. Daniel Morinigo-Sotelo
Dr. Tomas Garcia-Calva
Dr. Karen Julieth Uribe Murcia
Guest Editors
Manuscript Submission Information
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Keywords
- electrical machine
- state estimators
- condition monitoring
- data-driven models
- physics-based models
- digital twins
- residual signals
- hybrid models
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