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

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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