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Energies
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Novel Approaches to Electrical Machine Fault Diagnosis: Volume II
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Novel Approaches to Electrical Machine Fault Diagnosis: Volume II

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 4042

Special Issue Editors

Dr. Toomas Vaimann

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Guest Editor
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: electrical machines and diagnostics of electrical machines
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anton Rassõlkin

E-Mail Website
Guest Editor
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: electrical drives; mechatronics; electric vehicles; electrical machines
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

Special Issue Information

Dear Colleagues,

Electrical machines play an important role in the industry, performing crucial tasks in production, propulsion, power generation, and numerous other industrial processes. Hence, condition monitoring and the prognosis and diagnosis of the faults threatening the smooth operation of the machines are of utmost importance, as they can lead to minimizing downtime, avoiding financial loss and the threat to human life. as well as preserving the environment.

There are many traditional diagnostic techniques in use and under investigation today, yet with the world and technology moving rapidly forward, new horizons are also opening in the diagnostic field. The possibility of using the Internet of Things, powerful Artificial Intelligence tools, virtual sensors, cloud computing, and all the different technological solutions classified as Industry 4.0 options, more advanced, complex, but at the same time more precise diagnostic techniques can be used. There is a great opportunity for novel diagnosis approaches for electrical machines to be introduced, and at the same time, old and previously not very promising techniques can find new life due to advanced IT solutions, providing more computational resources and faster calculation and simulation times.

As the use of electrical machines in the world is rising rapidly in all the sectors of life, novel approaches to electrical machine fault diagnosis can show the way towards a more efficient use and prolonged lifetime for machines and lead to the introduction of different intelligent technologies in engineering.

Dr. Toomas Vaimann
Prof. Dr. Anton Rassõlkin
Prof. Dr. Jose Alfonso Antonino-Daviu
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. Energies 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.

Keywords

  • fault diagnosis
  • electric machines
  • condition monitoring
  • operation monitoring
  • prognosis of faults
  • diagnostic techniques
  • fault models
  • diagnostic models
  • steady state operation
  • transient operation
  • predictive maintenance
  • signal processing
  • fault tolerant control

Related Special Issue

Published Papers (2 papers)

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Research

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13 pages, 3986 KiB  
Article
Voltage-Induced Heating Defect Detection for Electrical Equipment in Thermal Images
by Ying Lin, Zhuangzhuang Li, Yiwei Sun, Yi Yang and Wenjie Zheng
Energies 2023, 16(24), 8036; https://doi.org/10.3390/en16248036 - 13 Dec 2023
Viewed by 577
Abstract
Voltage-induced heating defect is a type of defect that may occur in transformation substation equipment. Although this type of defect is less common compared to current-induced heating defects, it is crucial to identify it due to its association with severe insulation degradation problems [...] Read more.
Voltage-induced heating defect is a type of defect that may occur in transformation substation equipment. Although this type of defect is less common compared to current-induced heating defects, it is crucial to identify it due to its association with severe insulation degradation problems that require prompt intervention. However, the temperature variations caused by these defects may be relatively subtle, making it challenging to distinguish them in thermal images. In this work, considering the characteristics of voltage-induced heating defects and the scarcity of defect data, we propose a two-stage method for defect detection. In the first stage, we employ oriented R-CNN to detect oriented parts of the equipment, accurately localizing the centerline of each part. In the second stage, we extract the temperature distribution along the centerline of specific parts and discretize them as features. Subsequently, we train one-class support vector machines based on the features extracted from normal images for defect diagnosis. Experimental results demonstrate that the proposed method is capable of accurately detecting defects while maintaining a low false positive rate. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis: Volume II)
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Review

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44 pages, 3857 KiB  
Review
State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions
by Siddique Akbar, Toomas Vaimann, Bilal Asad, Ants Kallaste, Muhammad Usman Sardar and Karolina Kudelina
Energies 2023, 16(17), 6345; https://doi.org/10.3390/en16176345 - 01 Sep 2023
Cited by 7 | Viewed by 3150
Abstract
Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an [...] Read more.
Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics. Full article
(This article belongs to the Special Issue Novel Approaches to Electrical Machine Fault Diagnosis: Volume II)
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