Mahmoud Sayed Mahmoud Eid has followed the PhD Programme at Faculty of Engineering and Science, specialisation in Engineering Sciences, scientific field Mechatronics.
- Trial lecture starts at 10:15
- Public defence starts at 12:15
Title of trial lecture: Comparison of existing methods and future development of condition monitoring and fault diagnosis of electrical machines
Read the thesis in Norwegian Research Information Repository
Disputation chair: Paul Ragnar Svennevig
Assessment committee:
- First opponent: Professor Konstantinos Gryllias, KU Leuven, Belgium
- Second opponent: Professor Jonas Kristiansen Nøland, NTNU, Norway
- Chair of assessment committee: Professor Joao Leal, UiA
Supervisors in the doctoral work:
- Main supervisor: Professor van Khang Huynh, UiA
- Co-supervisor: Professor Kjell Gunnar Robbersmyr, UiA and Associate professor Jagath Sri Lal Senanayaka, NMBU
Summary of thesis
With the rapid advancement of the digitization of transportation and energy systems, smart sensors and interconnected platforms now enable real-time condition monitoring (CM) and predictive maintenance. This shift is transforming maintenance strategies from reactive repairs to proactive interventions, thereby enhancing system reliability and meeting the stringent safety demands of Industry 4.0.
Permanent magnet synchronous motors (PMSMs) have emerged as the preferred choice for modern electric drive systems—particularly in large-scale wind turbines and traction applications—due to their high efficiency, torque density, and dynamic response. To operate these motors effectively, voltage source inverters, especially three-level topologies, are widely employed to deliver high-quality AC waveforms with low harmonic distortion while enabling precise control of speed and torque. Despite these advancements, existing fault diagnosis methods continue to face significant challenges due to variable operating conditions, the presence of multiple faults, and noisy environments, all of which limit their diagnostic accuracy. Additionally, the scarcity of historical fault data in practical applications further constrains the effectiveness of data-driven approaches, which typically require large quantities of labeled samples. Addressing these challenges has thus become a central focus of current research in the field of fault detection and diagnosis (FDD).
This dissertation proposes six novel data-driven FDD models specifically tailored for inverter-fed PMSMs. These models are developed with three key objectives: first, to improve diagnosis accuracy for multiple faults under diverse operating conditions; second, to enhance the robustness of the models in noisy environments; and third, to achieve reliable fault diagnosis even when only a limited number of fault samples are available. The proposed models are described theoretically and validated through a combination of finite-element analysis models, experimental setups, and real-time hardware-in-the-loop (HIL) experiments. The experimental results demonstrate that the six models collectively achieve robust and efficient FDD performance with a low computational burden, making them well-suited for deployment in predictive maintenance strategies for PMSM drives. This, in turn, directly supports the reliability, efficiency, and safety requirements of smart industrial applications. To further validate the proposed approaches, comparative studies with other data-driven methods are conducted, demonstrating the superior effectiveness of the developed schemes. A brief summary of the models and their key findings is presented within this dissertation, while detailed explanations and results are provided in the appended papers.
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Opponent ex auditorio:
Deadline for the public to pose questions is during the break between the two opponents. Questions ex auditorio can be submitted to Kristine Evensen Reinfjord.
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