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Machines
Special Issues
Condition-Based Monitoring of Electrical Machines
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Condition-Based Monitoring of Electrical Machines

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 3159

Special Issue Editors

Prof. Dr. Roque A. Osornio-Rios

E-Mail Website
Guest Editor
HSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico
Interests: condition monitoring; power quality; fault diagnosis; signal processing; vibration analysis; electrical power engineering; control theory; instrumentation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jose Alfonso Antonino-Daviu

E-Mail Website1 Website2
Guest Editor
Department of Electrical Engineering, Universitat de València, 46022 Valencia, Spain
Interests: electric motors; fault diagnosis; transient analysis; signal processing; wavelet analysis; infrared thermography; time-frequency transforms
Special Issues, Collections and Topics in MDPI journals
Dr. Arturo Y. Jaen-Cuellar

E-Mail Website
Guest Editor
HSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio, Mexico
Interests: FPGA; Signal processing; Digital systems; Embedded systems; Genetic algorithms

Special Issue Information

Dear Colleagues,

In electrical machines and their applications in industry, condition monitoring is the basis for predictive maintenance. Machine health monitoring is a process of verifying the health of machinery during its normal operation. It is based on data acquisition, its processing and its comparison with trend and representative data from similar machines. In recent years, various machine health monitoring techniques have emerged that are used to determine the machine condition; additionally, advancements related to sensors, software and hardware are essential to achieve this goal. However, the topic continues to generate new trends in methodologies related to condition-based monitoring. The goal of this Special Issue is to bring researchers and industrial practitioners together to share their research findings and present ideas that are relevant in the field of electrical machine monitoring for determination of machine condition. 

Prof. Dr. Roque A. Osornio-Rios
Prof. Dr. Jose Alfonso Antonino-Daviu
Dr. Arturo Y. Jaen-Cuellar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Machines is an international peer-reviewed open access monthly 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 2400 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.

Keywords

  • condition detection
  • condition diagnosis
  • sensor fusion system
  • novelty detection
  • data mining
  • monitoring algorithms
  • electrical machines
  • induction motors

Published Papers (2 papers)

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Research

16 pages, 1955 KiB  
Article
Feature-Based Bearing Fault Classification Using Taylor–Fourier Transform
by Gerardo Avalos-Almazan, Sarahi Aguayo-Tapia, Jose de Jesus Rangel-Magdaleno and Mario R. Arrieta-Paternina
Machines 2023, 11(11), 999; https://0-doi-org.brum.beds.ac.uk/10.3390/machines11110999 - 29 Oct 2023
Viewed by 965
Abstract
This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor–Fourier transform (DTFT) and statistical methods. The DTFT allows the application of narrow bandwidth digital filters located in the spurious current [...] Read more.
This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor–Fourier transform (DTFT) and statistical methods. The DTFT allows the application of narrow bandwidth digital filters located in the spurious current signal components, wherewith it is possible to gain information to detect bearing issues and classify them using statistical methods. The methodology was implemented in MATLAB using the digital Taylor–Fourier transform for three fault types (bearing ball damage, outer-race damage, and corrosion damage) at different powering conditions: power grid source at 60 Hz and adjustable speed drive applied (60 Hz, 50 Hz, 40 Hz, 30 Hz, 20 Hz, and 10 Hz) in loading and unloading conditions. Results demonstrate a classification accuracy between 93–99% for bearing ball damage, 91–99% for outer-race damage, and 94–99% for corrosion damage. Full article
(This article belongs to the Special Issue Condition-Based Monitoring of Electrical Machines)
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24 pages, 4797 KiB  
Article
Hardware-in-the-Loop Scheme of Linear Controllers Tuned through Genetic Algorithms for BLDC Motor Used in Electric Scooter under Variable Operation Conditions
by Leonardo Esteban Moreno-Suarez, Luis Morales-Velazquez, Arturo Yosimar Jaen-Cuellar and Roque Alfredo Osornio-Rios
Machines 2023, 11(6), 663; https://0-doi-org.brum.beds.ac.uk/10.3390/machines11060663 - 19 Jun 2023
Cited by 2 | Viewed by 1512
Abstract
Outrunner brushless DC motors (BLDC) are a type of permanent magnet synchronous motor (PMSM) widely used in electric micro-mobility vehicles, such as scooters, electric bicycles, wheelchairs, and segways, among others. Those vehicles have many operational constraints because they are driven directly by the [...] Read more.
Outrunner brushless DC motors (BLDC) are a type of permanent magnet synchronous motor (PMSM) widely used in electric micro-mobility vehicles, such as scooters, electric bicycles, wheelchairs, and segways, among others. Those vehicles have many operational constraints because they are driven directly by the user with light protective wearing. Therefore, to improve control strategies to make the drive safer, it is essential to model the traction system over a wide range of operating conditions in a street environment. In this work, we developed an electro-mechanical model based on the Hardware-in-the-Loop (HIL) structure for a two-wheeler electric scooter, using the BLDC motor to explore its response and to test linear controllers for speed and torque management under variable operating conditions. The proposed model includes motor parameters, power electronics component characteristics, mechanical structure, and external operating conditions. Meanwhile the linear controllers will be adjusted or tuned though a heuristic approach based on Genetic Algorithms (GAs) to optimize the system’s response. The HIL scheme will be able to simulate a wide range of conditions such as user weight, slopes, wind speed changes, and combined conditions. The designed model can be used to improve the design of the controller and estimate mechanical and electrical loads. Finally, the results of the controller tests show how the proposed cascade scheme, tuned through the GA, improves the system behavior and reduces the mean square error with respect to a classical tuning approach between 20% and 60%. Full article
(This article belongs to the Special Issue Condition-Based Monitoring of Electrical Machines)
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