Advancements in Condition Monitoring of Electric Motors: Integrating Digital Twins, AI, and IoT for Enhanced Operational Efficiency, Fault Diagnosis, and Cybersecurity

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5782

Special Issue Editors


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Guest Editor
Department of Production and Management Engineering, Democritus University of Thrace, Vas. Sofias 12, GR-67100 Xanthi, Greece
Interests: electric power systems; modeling; design and control of electric motors; condition monitoring; fault diagnosis
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Special Issue Information

Dear Colleagues,

The role of electric motors in powertrains is central, making them essential in a wide range of industrial applications. Yet, their continuous operation and stress in extreme operating conditions poses a significant risk, potentially disrupting the production process. Moreover, aside from the consequences on production, the occurrence of failures can pose significant safety hazards for the workforce. With a view to minimizing the chances of unexpected consequences occurring, it is necessary to focus on the analysis and implementation of monitoring techniques, under any operating condition. In addition, in order to minimize maintenance costs and improve productivity, predictive maintenance is essential to accurately assess the severity of a potential failure in the near future. To this end, the integration of digital twins, Artificial Intelligence (AI), and Internet of Things (IoT) in condition monitoring of electric machines constitutes a cutting-edge approach. By leveraging real-time data from IoT sensors, AI algorithms can analyze the digital twin’s virtual representation of the electrical machine, enabling proactive identification of potential issues and optimizing maintenance strategies for improved operational efficiency and reliability. Furthermore, the security of critical information transmission and data protection from unauthorized users poses a challenge for the development of innovative solutions to enhance security, with a focus on wireless sensor networks (WSNs) and secure data transmission to electric motors.

The aim of this Special Issue is to contact and highlight research developments in key aspects such as i) techniques for continuous monitoring of the operational status of electric machines; ii) the collection and processing of large volumes of data in real and continuous time using IoT technology; iii) the correct placement of sensors in the motor so that data are collected accurately; iv) the detection, diagnosis, and prognosis of faults; v) digital twins-enabled condition monitoring; vi) AI-assisted fault diagnosis; vii) secure data transfer to avoid unforeseen interference; viii) development of advanced security mechanisms for WSNs in industrial applications; ix) ensuring the integrity, confidentiality, and availability of data transmitted between motors; x) minimizing vulnerabilities and weaknesses of digital transformation systems; and xi) improving resilience to fault diagnosis and cyberattacks.

Prof. Dr. Antonios Gasteratos
Prof. Dr. Theoklitos Karakatsanis
Guest Editors

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Keywords

  • electric motors
  • industrial applications
  • fault diagnosis
  • prognosis
  • predictive maintenance
  • smart sensors
  • condition monitoring
  • data collection and security challenges
  • digital twins
  • artificial intelligence
  • secure communication protocols
  • critical infrastructure protection
  • wireless sensor networks (WSNs)

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Published Papers (3 papers)

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Research

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26 pages, 1250 KiB  
Article
Online Algebraic Estimation of Parameters and Disturbances in Brushless DC Motors
by David Marcos-Andrade, Francisco Beltran-Carbajal, Alexis Castelan-Perez, Ivan Rivas-Cambero and Jesús C. Hernández
Machines 2025, 13(1), 16; https://doi.org/10.3390/machines13010016 - 30 Dec 2024
Viewed by 781
Abstract
Parameter identification in dynamical systems is a well-known problem with many applications in control design, system monitoring, and fault detection. As these systems are increasingly integrated into complex and demanding environments, challenges such as rapid response, uncertainty handling, and disturbance rejection must be [...] Read more.
Parameter identification in dynamical systems is a well-known problem with many applications in control design, system monitoring, and fault detection. As these systems are increasingly integrated into complex and demanding environments, challenges such as rapid response, uncertainty handling, and disturbance rejection must be addressed. This paper presents a real-time estimation technique for parameters and load torque in brushless DC (BLDC) motors. These electrical machines are extensively used in engineering applications and often operate under hard conditions. The proposed method is based on algebraic identification, known for its robust performance in both linear and nonlinear systems. In utilizing the mathematical model of a BLDC motor, a set of equations is derived to enable parameter estimation, assuming the availability of input and output measurements in open loop. Moreover, unknown load torque is estimated by approximating the disturbance over a short time window using Taylor series expansion polynomials. The theoretical contribution is analytically validated and is also verified through numerical evaluations revealing the effectiveness of the proposed technique for real-time parameter and disturbance estimation in BLDC motors over other important techniques. Additionally, to address potential peaks in the estimation process, a modification involving an exponent is introduced to mitigate these issues. Full article
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16 pages, 3019 KiB  
Article
Hydrodynamic Performance Study of a Reciprocating Plate Column Dirven by Electro-permanent Magnet Technology
by Kai Guo, Jianxu Jiang, Deqiang Zhang, Linyuan Meng, Yiran Zhang, Xiantao Fan and Hongsheng Zhang
Machines 2024, 12(5), 330; https://doi.org/10.3390/machines12050330 - 13 May 2024
Viewed by 1296
Abstract
The reciprocating plate column is a kind of column with the plates driven by a geared motor, and it has advantages in regard to efficiency compared to traditional columns in the extraction process, however, it comes with an increase in energy consumption. A [...] Read more.
The reciprocating plate column is a kind of column with the plates driven by a geared motor, and it has advantages in regard to efficiency compared to traditional columns in the extraction process, however, it comes with an increase in energy consumption. A new type of reciprocating plate column driven by electro-permanent magnet technology (EPM) is proposed in this paper to obtain a better performance with lower energy consumption. The feasibility and performance of the proposed column is studied by numerical simulation and experiments with a kerosene–water system. The electro-permanent magnet chuck could provide a maximum amplitude of 12 mm in this study. Kerosene was used as the dispersed phase, and deionized water was used as the continuous phase, in a laboratory-scale 35 mm diameter reciprocating plate column driven by EPM. Hydrodynamic performance experiments were carried out with different flowrates of both phases and reciprocating frequencies. The experimental results show that the electro-permanent magnet chuck, which serves as the driving device of the reciprocating plate column, plays the role of adding energy and increasing the droplet breakage. In addition, the energy consumption of the reciprocating plate column with traditional geared motor and electro-permanent magnet chuck is calculated respectively. Compared with the traditional geared motor, the energy saving of the electro-permanent magnet chuck is as high as 98.55%. Full article
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Review

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41 pages, 7143 KiB  
Review
Overview of IoT Security Challenges and Sensors Specifications in PMSM for Elevator Applications
by Eftychios I. Vlachou, Vasileios I. Vlachou, Dimitrios E. Efstathiou and Theoklitos S. Karakatsanis
Machines 2024, 12(12), 839; https://doi.org/10.3390/machines12120839 - 22 Nov 2024
Cited by 1 | Viewed by 1713
Abstract
The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being [...] Read more.
The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being incorporated in various industrial applications, especially in electrical machines as a means of control, monitoring and preventive maintenance. This paper is focused on reviewing the use PMSM in lift systems, the application of various condition monitoring techniques and real-time data collection techniques using IoT technology. In addition, we focus on different categories of industrial sensors, their connectivity and the standards they should meet for PMSMs used in elevator applications. Finally, we analyze various secure ways of transmitting data on different platforms so that the transmission of information takes into account possible unwanted instructions from exogenous factors. Full article
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