Guest Editors
Prof. Acácio Manuel Raposo Amaral
Email: amramaral@ieee.org
Affiliation: Department of Informatics and Systems, Polytechnic Institute of Coimbra, 3030-790, Coimbra, Portugal
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Research Interests: fault diagnosis in electronic systems, internet of things (IoT), machine learning, signal processing, and business intelligence
Summary
Achieving sustainability requires the widespread adoption of electrification across multiple sectors. In the primary sector, including agriculture and fishing, the integration of electric vehicles and drones enhances operational efficiency while reducing environmental impact. In the secondary sector, industrial electrification has become an irreversible trend, driven by the adoption of electrically powered machinery and processes. This transition not only optimizes energy efficiency but also significantly reduces greenhouse gas emissions. Similarly, the tertiary sector is witnessing a growing expansion of electrification, with the increasing use of electric vehicles in public transportation and the adoption of energy-efficient electrical equipment in industries such as healthcare and commerce.
As electrification advances rapidly, ensuring system reliability and fault tolerance has become a critical challenge. Components such as power converters, electrical machines, sensors, and batteries are susceptible to failures, which can lead to operational disruptions, reduced efficiency, and, in critical systems, serious safety risks. Therefore, developing effective signal processing techniques for fault diagnosis is essential to enable early failure detection, minimize unexpected shutdowns, and ensure the safe and efficient operation of electrified systems.
This special issue invites high-quality research contributions on signal processing methods for fault diagnosis in electrified systems. Topics of interest include, but are not limited to:
· Advanced signal processing techniques for fault detection and diagnosis
· Application of machine learning and artificial intelligence in fault diagnosis
· Condition monitoring and predictive maintenance of electrical and electronic systems
· Signal-based fault detection in power converters, electrical machines, and batteries
· IoT-based fault diagnosis and real-time monitoring in electrified infrastructures
We welcome both original research articles and comprehensive review papers that contribute to the advancement of fault diagnosis using signal processing techniques.
Keywords
internet of things, machine learning, data processing, microcontrollers, software, connectivity, data security, sustainability, smart cities, smart agriculture and fault diagnosis
Published Papers
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