Condition Monitoring and Predictive Maintenance in Industrial Systems
Applied Statistics and Data Analysis for Engineering
Vibration Analysis and Dynamic System Modeling
Signal Processing Techniques for Fault Diagnosis
Machine Learning Applications in Engineering Maintenance
Dr. Hamid Shiri is a Research Fellow at the University of Southampton, affiliated with the Electrical Power Engineering group and the Tony Davies High Voltage Laboratory. He earned his PhD in Engineering from Wrocław University of Science and Technology, where he was awarded a prestigious Marie Skłodowska-Curie Fellowship under the H2020-MOIRA project.
Dr. Shiri's research focuses on signal processing, machine learning, and uncertainty quantification for the condition monitoring and predictive maintenance of complex mechanical and electrical systems. His expertise includes robust statistical methods, non-Gaussian noise modeling, and data-driven diagnostics and prognostics for long-term monitoring applications.
He is particularly interested in data-driven approaches and the development of probabilistic models for system degradation and fault prediction. He is currently working on condition-based maintenance with a focus on high-voltage cable systems, particularly the long-distance transmission cables used in modern power networks.
Dr. Shiri has collaborated with both academic and industrial partners across Europe and has held visiting research positions in Sweden and Poland.