Signal Processing for Fault Detection and Diagnosis in Electric Machines and Energy Conversion Systems

Dear Colleagues,

Electrical machines and energy conversion systems in general have become increasingly important over the last few decades. With the aim to achieve sustainability, the electrification of a wide range of applications is advancing, including in the consumer and industry sector, road vehicles and marine vessels. Electric machines are used almost everywhere either as motors or as generators. Meanwhile, modern energy conversion systems rely on power electronics to provide good performance, efficiency, and power quality.

As electric energy conversion systems and electric drives become more sophisticated, the appearance of an unpredicted fault may result in abnormal operation or system shutdown, decreasing its reliability. Therefore, timely fault diagnosis has become a prerequisite component to achieve reliability or fault-tolerant operation. The main task of a fault diagnosis methodology is to provide a warning when a problem (a fault) is detected in a system, and even detect the source of this fault. This is mostly achieved via signal processing methods, which analyze the electrical and/or mechanical quantities of the system to detect and locate the fault. In this regard, information obtained using mechanical and/or electrical sensors has to be processed. In the final step, fault indication and classification are provided, either as a result of frequency or time–frequency analysis of the signals or using artificial intelligence and machine learning methodologies.

In this Special Issue, unpublished original papers and reviews focused on (but not restricted to) the following research areas will be considered for publication:

• Signal processing techniques for condition monitoring, fault detection and diagnosis of electric machines and drives;
• Fault detection and diagnosis of power electronic converters;
• Fault detection and diagnosis of energy conversion systems;
• Signal processing methods for fault detection;
• Signal processing methods for fault-tolerant systems;
• Artificial intelligence and machine learning methods for fault detection and diagnosis of electric machines and energy conversion systems.

Dr. Epaminondas D. Mitronikas
Guest Editor

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