Master’s Thesis Defense: Hybrid Neural-Fuzzy System for Robotic Fault Detection
On Thursday, September 12, 2019, at 9:00 AM, Arkan Ali Jassim defended his master’s thesis titled:
“Design of a Hybrid Neural-Fuzzy System for Fault Detection and Isolation in Robotic Arms.”
The defense was held in Dijla Hall, with a committee that included:
- Assist. Prof. Dr. Hadeel Nasrat Abdullah (Chair)
- Assist. Prof. Dr. Saad Mohammed Saleh
- Assist. Prof. Dr. Abdul Raheem Diyab Hamoud
- Assist. Prof. Dr. Abbas Hussein Eisa (Supervisor).
The research utilized backpropagation neural networks (BPNN) for creating mathematical models to detect and isolate faults in robotic arm components. The method involved comparing output data with the model to identify residual signals indicating faults. The thesis was accepted with minor revisions, and the candidate was awarded a “Pass” grade.
