Application of Machine Learning Techniques for Bearing Fault Diagnosis

Machine learning enhances machine diagnostics through advanced data analysis, pattern recognition, and fault prediction. This study investigates the application of machine learning algorithms for bearing fault detection. The objective is to develop intelligent methodologies for the predictive diagnosis of bearing faults in rotating machinery, emphasizing the significance of timely intervention to prevent critical failures. The methodology employed encompasses a systematic approach, including data preprocessing, feature extraction, and model development. This research employs advanced machine learning techniques, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms, in conjunction with time-domain and frequency-domain feature extraction methods. The implemented approach substantially enhances fault detection accuracy, achieving an aggregate classification precision of 97.8% across all fault categories. Notably, the SVM algorithm demonstrates exceptional performance, attaining a 99.2% accuracy rate in inner-race fault identification. This investigation provides a comprehensive analysis of the Case Western Reserve University (CWRU) dataset, data preprocessing procedures, feature extraction techniques, and machine learning algorithms utilized for fault detection. The results emphasize the effectiveness of these algorithms in bearing fault diagnosis, offering valuable insights for predictive maintenance strategies in industrial applications. This research also aligns with the objectives of Industry 4.0, which focuses on utilizing intelligent, automated systems to enhance factory efficiency and reliability. The study concludes by proposing future research directions to further advance these technologies and support the transition toward more intelligent, interconnected industries.