Advances in Fault Detection, Diagnosis and Prognosis in Industrial Motors—2nd Edition
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: 31 August 2024 | Viewed by 758
Special Issue Editor
Interests: electrical machines design; analysis, modeling, optimization and fault diagnosis of electrical machines; controller design; artificial intelligence methods application to electrical machines
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Electric motors are widely used in numerous industrial applications. They operate continuously and for long-term periods both at nominal and overload conditions. As such, it is evident that the occurrence of faults is quite frequent. A possible motor failure can lead to temporary shutdown or interruption of the production process, which results in a loss of services and/or supplies. Additionally, the Industry 4.0 framework strongly supports smart manufacturing, complying with sustainability of all the involved systems and operations. Thus, it is of great importance to proceed to fast and reliable assessment of the health status of industrial drives. The development of effective mechanisms for electric motor fault detection has therefore attracted widespread attention from both academical and industrial fields. The goal of this issue is to bring researchers together to share their research findings and present attractive perspectives in the fields of fault detection, diagnosis, and prognosis in industrial motors. Prospective authors are invited to submit original and high-quality papers. Topics of interest include but are not limited to the following areas:
- Advanced diagnostic approaches for mechanical (e.g., bearings, gearbox, shaft bending, static and dynamic eccentricity), electrical (short circuits, winding interruption, asymmetry in supply voltage, voltage fluctuation, insulation failure, etc.), and electromechanical (rotor bars breaking, rotor end-ring detachment, etc.) faults;
- Diagnosis of multiple simultaneous faults;
- Early detection of incipient faults and fault isolation;
- Multisensor data fusion;
- Line- and inverter-fed electrical machines;
- Signal analysis and faults diagnosis during motor operation under harsh conditions;
- Non-invasive techniques;
- Predictive maintenance and real-time condition monitoring systems;
- Discrimination between faulty conditions and healthy conditions under the presence of load oscillations or speed variation;
- Modern signal processing techniques toward information quality improvement;
- Enhanced pattern recognition algorithms;
- Advanced fault detection and diagnosis methods based on artificial intelligence (e.g. supervised/unsupervised machine learning).
Prof. Dr. Yannis L. Karnavas
Guest Editor
Manuscript Submission Information
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Keywords
- electrical machines
- industrial motors
- faults detection
- diagnosis
- artificial intelligence
- predictive maintenance
- industry 4.0
