Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines

Dear Colleagues,

Electrical machines are the key components of many industrial processes, as well as in everyday life. They may provide mechanical power (e.g., induction motors, permanent magnet motors) or electrical power (e.g., synchronous generators, wind turbines), and their vital role has increased along with the growing widespread use of electrical vehicles, renewable energies, robots, drones, etc. A growing trend is the integration of electrical machines in information systems aimed at tracking production data, optimizing their functional setup, or assessing the condition of the machine in order to prevent or minimize the impact of sudden failures. For this integration to succeed, it is necessary to analyze diverse sensor parameters (currents, vibrations, axial fluxes, speed, etc.) using signal processing techniques, and to present this information to the end user while taking into account the different information channels available in modern communication systems (specialized SCADA systems, web pages, mobile apps, cloud repositories, etc.).

Therefore, in recent years, the fault diagnosis and monitoring technologies of electrical machines have attracted increasing attention from both academia and industry. Both high volumes and high quality of information are being demand from multiple types of sensor data, but sensors are also subject to failure, which must be accounted for in the diagnostic systems. The integration of distributed sensor networks in model-based, signal-based, knowledge-based, and hybrid/active diagnostic systems is a challenging issue which requires expertise from a broad set of disciplines, such as artificial intelligence, adaptive observer design, statistical estimation, data dimension reduction techniques, etc. On the other side, the acquired information can be stored, processed, and delivered using modern cloud-based software services and big-data technologies.

We invite researchers from both academia and industry to submit original and unpublished manuscripts to this Special Issue to showcase some of the recent developments within these topics.

The goal of the Special Issue is to publish the most recent research results and industrial applications of sensors in fault diagnosis and monitoring of electrical machines. Topics that are suitable for this Special Issue include, but are not limited to: 

• Data-driven and model-based sensor fault diagnosis;
• Integration of high-volume sensor data in the design of applications for fault diagnosis of electrical machines and drives;
• Sensors in advanced electrical machines—fault diagnosis and monitoring applications in different industrial sectors;
• Methods, concepts, and performance assessment for improving the fault diagnosis of existing techniques in the field of electrical machines;
• Electrical drives as sensors in industrial processes;
• Cloud-based software services for fault diagnosis and monitoring of electrical machines.

Prof. Dr. Martin Riera-Guasp
Prof. Dr. Manuel Pineda-Sanchez
Prof. Dr. Javier Martinez-Roman
Prof. Dr. Ruben Puche-Panadero
Prof. Dr. Angel Sapena-Bano
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.