Fahad Alharbi is now pursuing the Ph.D. degree with the School of Information and Physical Sciences, University of Newcastle, Callaghan, NSW, Australia. His research focuses on acoustic and sound analysis in rotating machinery, with a primary interest in idler components. In his research, he incorporates machine learning and deep learning techniques, particularly fault detection. His work involves extracting and analyzing audio features to identify and diagnose faults in machinery, enhancing predictive maintenance strategies. Through his research, he aims to contribute to developing more efficient and reliable fault detection systems