معرفی
Hatem Algabroun serves as a Senior Lecturer in the Department of Mechanical Engineering at Linnaeus University's Faculty of Engineering. He earned his PhD in Mechanical Engineering from the same institution in 2020, with research focused on maintenance engineering and industrial digitalization.
His academic profile spans both teaching and research domains. Algabroun teaches across all academic levels, from bachelor's courses like Reliability and Maintenance Technology and Material Planning and Production Control I to master's level courses including Condition Monitoring and Predictive Maintenance, and doctoral-level seminars on Swedish maintenance research perspectives.
Research-wise, Algabroun specializes in the critical intersection of predictive maintenance, condition monitoring, and industrial digitalization, with particular emphasis on developing practical solutions for small and medium-sized enterprises. His work on adaptive sampling algorithms and self-adaptive software architectures represents significant contributions to Maintenance 4.0 frameworks.
His publication record shows consistent output through 2025, with recent work focusing on machine learning applications for IoT data collection, augmented reality training systems, and technological paradigm analysis in manufacturing. The thematic continuity across his publications demonstrates deep expertise in digital maintenance systems and industrial automation.
Algabroun actively participates in externally funded research projects including Project expert competence: Smart Industry, phase 2, Project: Smart Dat, and seed projects involving machine learning for material property prediction. He collaborates extensively with industry partners and academic colleagues from institutions across Europe.
As a member of the Smart Industry Group (SIG), Algabroun contributes to interdisciplinary research bridging computer science and mechanical engineering to develop smarter production systems. His work consistently addresses the practical implementation challenges of digital technologies in real manufacturing environments, particularly for resource-constrained SMEs.
