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Sensors for Fault Diagnosis of Electric Machines
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Sensors for Fault Diagnosis of Electric Machines

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1050

Special Issue Editor

Dr. Christos Zachariades

E-Mail Website
Guest Editor
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK
Interests: condition monitoring and diagnostics of high voltage equipment (design and optimisation of sensors for partial discharge (PD) detection, design of interfacing instrumentation for electrical condition monitoring, automation of condition diagnosis using artificial intelligence (AI)); overhead transmission lines (design and optimisation of insulation systems, compaction of high-voltage transmission lines, instrumentation for the condition assessment of insulators and conductors); high voltage test techniques (type approval testing of high-voltage equipment, design of test rigs and experimental procedures, finite-element analysis (FEA) simulations)

Special Issue Information

Dear Colleagues,

Electric machines, encompassing electric motors and generators, are indispensable parts of modern industrial processes due to their many benefits and wide range of applications. Therefore, maintaining the safety, efficiency, cost-effectiveness and operational reliability of electrical machines is essential. This can be realized through condition monitoring, which requires the application of sensors and fault diagnosis techniques.

Authors are invited to contribute original research articles, critical reviews and insightful case studies to this Special Issue of Sensors. This Special Issue aims to highlight recent advancements, innovative applications and emerging trends in sensor technology specifically tailored for fault diagnosis in electric machines. Contributions should provide significant insights, propose novel methodologies or present practical implementations in the field.

The topics of interest include, but are not limited to, the following:

  • Sensor technologies;
  • Wireless sensor networks;
  • Smart sensors and IoT-enabled diagnostics;
  • Signal processing methods for fault detection;
  • Machine learning and AI techniques in fault diagnosis;
  • Data fusion from multiple sensors;
  • Remote monitoring and diagnostics;
  • Predictive maintenance systems;
  • Sensor deployment and data acquisition;
  • Industrial applications and case studies;
  • Future trends in sensor technology for electric machines.

We look forward to your valuable contributions that will drive forward the field of fault diagnosis in electric machines through innovative sensor technologies. 

Dr. Christos Zachariades
Guest Editor

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. 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.

Keywords

  • condition monitoring
  • diagnostics
  • electric machine
  • fault detection
  • predictive maintenance
  • sensor
  • signal processing

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Published Papers (1 paper)

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Research

16 pages, 2387 KiB  
Article
A Generalized Convolutional Neural Network Model Trained on Simulated Data for Fault Diagnosis in a Wide Range of Bearing Designs
by Amirmasoud Kiakojouri and Ling Wang
Sensors 2025, 25(8), 2378; https://doi.org/10.3390/s25082378 - 9 Apr 2025
Cited by 1 | Viewed by 360
Abstract
Rolling element bearings (REBs) are critical components in rotating machinery and a leading cause of machine failures. Traditional fault detection methods rely on signal processing, but advances in machine learning (ML) and deep learning (DL) have dramatically improved diagnostic accuracy. However, existing DL [...] Read more.
Rolling element bearings (REBs) are critical components in rotating machinery and a leading cause of machine failures. Traditional fault detection methods rely on signal processing, but advances in machine learning (ML) and deep learning (DL) have dramatically improved diagnostic accuracy. However, existing DL models struggle with data availability, generalization, and domain adaptation, making industrial applications challenging. This study proposes a convolutional neural network (CNN) model trained on numerically simulated vibration data generated for a wide range of bearing designs. A novel hybrid signal processing method is employed to enhance feature extraction and reduce domain shifts between simulated and real-world data. The optimized CNN model, trained on simulated data, is tested using experimental and real-world vibration signals from laboratory bearings and jet engine components. The results show high classification accuracy using data from the Case Western Reserve University experimental dataset and successful fault detection in real-world Safran jet engine ground tests. The findings demonstrate the effectiveness of the developed CNN-based model for bearing fault classification, tackling training data scarcity and generalizability challenges while contributing to the development of intelligent fault diagnosis models for several industrial applications. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis of Electric Machines)
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