SI IMs 2024 : Special Issue Condition Monitoring and Fault Diagnosis of Induction Motors

Dear Colleagues,

Induction motors (IMs) are critical components in numerous industrial applications. Although they require only basic maintenance, unexpected breakdown and failures may occur due to their continuous operation under harsh conditions. The occurrence of faults can lead to a production shutdown, which may result in great economic losses. To avoid the above consequences, it is essential to develop robust mechanisms that permit: i) the regular condition monitoring of industrial drives, ii) the fast and reliable assessment of their health status and iii) the accurate determination of the fault severity. This allows the prognosis of early developing faults, which minimizes unscheduled downtime, improves productivity and reduces maintenance costs. The condition-based maintenance is also aligned with the context of Industry 4.0 and has attracted widespread attention. The goal of this Special Issue is to bring researchers together to share their research findings and present attractive perspectives in the fields of: a) online monitoring technologies for industrial drives, b) IMs fault detection, diagnosis and prognosis, c) analysis and processing of obtained information, d) IMs remaining useful life prediction and e) scientific support towards the establishment of new technical standards regarding machinery condition monitoring and predictive maintenance. This Special Issue encourages and welcomes both original research articles (with significant contribution to numerical, theoretical and/or experimental analysis) and review articles related to the aforementioned application areas. Topics of interest include but are not limited to the following:

Predictive maintenance and real-time condition monitoring systems;
On-line and remote location monitoring technologies;
Non-invasive techniques for data acquisition;
Robust methodologies for measurement, testing and diagnostics;
Modern methods for signal processing;
Enhanced pattern recognition algorithms;
Failure mechanism analysis;
Induction motors’ remaining useful life prediction;
Diagnostic approaches for all types of faults (i.e., mechanical, electrical, electromechanical, incipient, multiple simultaneous faults, etc.);
Advanced artificial intelligence based fault diagnosis methods;

Dr. Ioannis D. Chasiotis
Prof. Dr. Yannis L. Karnavas
Guest Editors

Special Issue "Condition Monitoring and Fault Diagnosis of Induction Motors"
https://www.mdpi.com/journal/machines/special_issues/fault_diagnosis_induction_motors