<![CDATA[This study investigates the application of machine learning techniques for predictive maintenance in industrial rotating machinery, with a specific focus on bearing fault detection. Utilizing a qualitative research approach, the study synthesizes existing literature, industry case studies, and experimental findings to provide a comprehensive analysis of data-driven maintenance practices. Vibration signal analysis, particularly using frequency domain features extracted through Fast Fourier Transform (FFT), forms the basis for fault diagnosis. Machine learning models such as Support Vector Machines (SVM) and ensemble classifiers are examined for their effectiveness in early detection of bearing faults. The results highlight that these models achieve fault identification accuracies exceeding 90%, enabling timely interventions that reduce unplanned downtime and maintenance costs. The qualitative insights also emphasize integration with Industry 4.0 technologies like IoT and cloud computing, which enhance real-time monitoring capabilities and scalability of predictive maintenance systems. Challenges, including data quality issues and environmental noise, are discussed alongside future directions for algorithm refinement and broader fault detection. Overall, this study underscores the value of machine learning-based predictive maintenance as a transformative approach for improving reliability and operational efficiency in industrial rotating machinery.]]>
