Machine Learning for Vibration-Based Fault Diagnosis – Training Services

Machine Learning for Vibration-Based Fault Diagnosis – Training Services introduces the integration of AI and data-driven approaches into vibration analysis for predictive maintenance and fault detection. The training covers vibration signal preprocessing, feature extraction techniques, and dimensionality reduction methods to prepare data for machine learning models. Participants will explore supervised and unsupervised learning algorithms such as decision trees, support vector machines, neural networks, and clustering for fault classification and anomaly detection. Case studies on rotating machinery, bearings, and gearboxes illustrate real-world applications. Practical sessions using Python, MATLAB, and ML libraries (scikit-learn, TensorFlow, PyTorch) provide hands-on experience in building, training, and evaluating diagnostic models.