Assistant Professor Dr. Mohammed Azhar Abdulatif Al-Obaidi and his research team have published a pioneering engineering article entitled “Real Time Fault Diagnosis in Industrial Robotics Using Discrete and Slantlet Wavelet Transformations”. The study integrates discrete and slantlet wavelet transformations with advanced artificial intelligence techniques to enable real-time fault diagnosis in industrial robotics, reflecting an effective combination of theory and practical application. The research was published in Scientific Reports, a peer-reviewed, open-access journal issued by the global publishing house Nature Portfolio within Springer Nature. The journal is recognized as one of the leading multidisciplinary journals worldwide, with distinguished metrics: Impact Factor (2024) [Read More]