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Energies
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Early Detection of Faults in Induction Motors II
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Early Detection of Faults in Induction Motors II

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 1960

Special Issue Editors

Prof. Dr. Daniel Morinigo-Sotelo

E-Mail Website
Guest Editor
HSPdigital-ITAP-ADIRE, University of Valladolid, 47002 Valladolid, Spain
Interests: induction motors; fault detection and diagnosis; condition monitoring; predictive maintenance; signal processing; smart grids; power quality
Special Issues, Collections and Topics in MDPI journals
Dr. Ignacio Martin-Diaz

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Valladolid, 47011 Valladolid, Spain
Interests: electrical engineering; renewable energies; data science and optimization applied to energy management; electrical equipment diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Induction motors are a crucial element in many industry fields, in transportation, and in the service and utility sector. Although they are considered robust machines, they are also subject to failures that, if not detected in time, can lead to catastrophic breakdowns. This can lead to increased costs for companies, unplanned production stops, destruction of facilities, or service interruptions.

For these reasons, the interest of industry and academia in developing early detection systems to prevent these incipient failures from evolving into catastrophic ones has been renovated and boosted. Techniques for early fault detection would allow the implementation of predictive maintenance systems, which are an essential element of Industry 4.0.

Condition monitoring of induction motors has been traditionally based on the analysis of the stator current or motor vibrations. However, currently, new solutions based on the analysis of other signals, such as stray flux, sound, and speed, have been proposed.

This Special Issue has therefore a broad scope, though it is focused on the induction motor. Submitted works may deal with the early detection of any type of fault in motors working in stationary or transient regimes and line- or inverter-fed. Innovative papers related to advanced signal processing techniques, machine learning, artificial intelligence, big data, and sensors will be welcome.

Prof. Dr. Daniel Morinigo-Sotelo
Dr. Ignacio Martin-Diaz
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

  • induction motors
  • condition monitoring
  • predictive maintenance
  • fault detection and diagnosis
  • early detection and diagnosis
  • detection in transient regimes
  • detection in steady-state regimes
  • line- and inverter-fed motors
  • signal processing for monitoring and diagnosis
  • smart sensors for monitoring electric motors
  • monitoring and diagnosis of electric motors in Industry 4.0

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Related Special Issue

Published Papers (1 paper)

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Research

21 pages, 13462 KiB  
Article
Optimization of Practicality for Modeling- and Machine Learning-Based Framework for Early Fault Detection of Induction Motors
by Moritz Benninger and Marcus Liebschner
Energies 2024, 17(15), 3723; https://doi.org/10.3390/en17153723 - 28 Jul 2024
Cited by 1 | Viewed by 1350
Abstract
This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of [...] Read more.
This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of electrical machines in various industrial applications. The main contributions of this paper include the elimination of a fingerprint measurement in the processing of the framework and the development of a generalized model for fault detection and diagnosis. These aspects allow the training of neural networks with a simulated data set before even knowing the specific induction motor to be monitored. The pre-trained feed-forward neural networks enable the detection of several electrical and mechanical faults in a real induction motor with an overall accuracy of 99.56%. Another main contribution is the extension of the methodology to a larger operating range. As a result, various faults in a real induction motor can be detected under different load conditions with accuracies of over 92%. As a further part of the paper, a concept for a prototype is presented, which enables the autonomous and practice-friendly application of the framework. Full article
(This article belongs to the Special Issue Early Detection of Faults in Induction Motors II)
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