Gearbox Fault Diagnosis

Gearbox fault diagnosis aims to detect and classify malfunctions in gearboxes using vibration analysis, preventing costly failures and downtime in industrial machinery. Current research emphasizes developing robust methods that handle noisy data and variable operating conditions, employing techniques like advanced signal processing (e.g., optimized filtering, calculus-enhanced energy operators) and machine learning (e.g., support vector machines, convolutional neural networks) for automated feature extraction and fault classification. These advancements are crucial for improving the reliability and efficiency of condition monitoring systems across various industries, particularly in wind energy and other sectors with large-scale gearbox deployments.