Muhamad Azhar Abdilatef Alobaidy

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About

Muhamad Azhar Abdilatef Alobaidy is a researcher specializing in fault diagnosis and condition monitoring for robotic and industrial systems, with a particular focus on signal processing techniques. His work addresses the critical challenge of maintaining operational reliability in robot systems, which are prone to mechanical and electrical faults over time. Alobaidy’s major contributions include comprehensive review studies that synthesize the state of the art in robot fault diagnosis, as well as novel methodological advances—most notably, the application of the Slantlet Transform (SLT) for detecting faults in robot arms. His 2020 review paper, “Faults Diagnosis in Robot Systems: A Review,” has garnered 27 citations, establishing a foundational reference in the field. More recently, his 2025 study on real-time fault diagnosis in industrial robotics using discrete and slantlet wavelet transformations demonstrates a practical, hardware-based framework for precision-driven environments. This work, along with his earlier 2022 paper on Slantlet transform application, highlights his commitment to developing efficient, real-time diagnostic tools. Alobaidy’s research is particularly valuable for students and engineers seeking to understand and implement robust fault detection methods in modern industrial robotics.