Acácio Manuel Raposo Amaral is a distinguished researcher and academic in electrical engineering, born in Luso, Angola, in 1974. He earned his Electrical Engineering diploma in 1998, followed by a Master’s degree in Energy in 2005, and a Doctorate in Engineering (specializing in Instrumentation and Control) in 2011, all from the University of Coimbra, Portugal. Since 1998, Amaral has been affiliated with the Polytechnic Institute of Coimbra, where he serves as an Assistant Reader in the Department of Informatics and Systems.

Amaral’s research centers on fault diagnosis and the design of switch-mode power supplies, with a particular focus on the effects of aging in electrolytic capacitors and the development of innovative diagnostic and preventive solutions. His prolific scholarly output includes numerous journal articles and conference papers on topics such as machine learning-based fault detection, simulation tools for power electronics, and advanced diagnostic algorithms for electronic components.

In addition to his research, Amaral is the author of several influential textbooks in the field of electronics, including "Eletrónica Aplicada" (2021), "Eletrónica Digital – Fundamentos e Projeto" (2019), and "Electrónica Analógica – Princípios, Análise e Projectos" (2017), which are widely used in Portuguese engineering education. His contributions extend to collaborative works on capacitor diagnostics and power electronics, reflecting a sustained commitment to advancing both academic research and practical engineering education in Portugal and beyond

Acácio Manuel Raposo Amaral’s research interests focus primarily on fault diagnosis and fault tolerance in electrical and electronic systems, especially in power converters and storage devices. His work emphasizes the design, simulation, and fault detection of power converters such as DC-DC converters and switch-mode power supplies. A significant part of his research addresses the aging and degradation of storage components like electrolytic capacitors, supercapacitors, and batteries, aiming to develop diagnostic and preventive solutions to extend their operational life.

He applies advanced signal processing techniques—including Discrete Fourier Transform (DFT), Short-Time Fourier Transform (STFT), Wavelet Transforms (DWT), and Empirical Mode Decomposition (EMD)—combined with machine learning algorithms such as Linear Regression, Decision Tree Regression, Random Forest Regression, Logistic Regression, and Random Forest Classification to monitor and predict component health and failures.

His research also involves developing offline fault diagnosis techniques to assess degradation levels in storage devices and improve reliability in electrical machines, power electronics, and drives. Amaral’s interdisciplinary approach integrates instrumentation, control, and energy systems to enhance the robustness and fault tolerance of electromechanical systems.

In summary, his core research areas include fault diagnosis, power converter design and simulation, aging effects on capacitors and batteries, and the application of machine learning and signal processing for condition monitoring and predictive maintenance in electrical engineering systems