High-accuracy gearbox fault detection using deep learning on vibrational data

In the face of escalating demands for reliability in industrial machinery, this study introduces an advanced deep-learning model aimed at the early detection of faults in rotating gear systems. Addressing the need for timely fault diagnosis to prevent operational disruptions and financial setbacks, our work utilizes a novel neural network approach applied to vibrational data from Kaggle's Gearbox Fault Diagnosis Dataset. The methodology involves data preprocessing steps, including normalization and one-hot encoding, followed by training a sequential neural network with multiple dense layers and dropout for regularization. The model, trained using the Adam optimizer and evaluated over 50 epochs, significantly outperforms traditional machine learning techniques, achieving a remarkable 98.63% accuracy in distinguishing between healthy and compromised gear conditions. This research marks a significant stride in predictive maintenance, providing a robust foundation for future development toward prognostic health management systems that anticipate maintenance needs and ensure uninterrupted industrial productivity.