A Comprehensive Approach for Gearbox Fault Detection and Diagnosis Using Sequential Neural Networks

Abstract

Gearbox faults can lead to significant damage and downtime in industrial machinery, resulting in substantial losses for manufacturers. Detection of faults in gears in the incipient state is essential to ensure safe and reliable operation of industrial machineries. In recent years, there has been an increasing interest in using machine learning algorithms to automate gearbox fault detection. This paper proposes a machine learning approach for identifying different categories of faults in a gearbox based on vibration signals. The proposed method was evaluated on a dataset of vibration signals collected from a two-stage gearbox under different operational conditions. The research is focused on developing a sequential neural network-based method for detecting multiple gear faults simultaneously. The results showed that the developed method achieved high training and validation accuracies and relatively low training and validation losses, indicating the model's ability to accurately detect and classify faults in gearboxes. The testing accuracies were also high, demonstrating the model's ability to generalize well to new data. The practical implications of the research are significant for improving the reliability and maintenance of gearboxes in various industrial applications. The developed method has the potential to reduce downtime, maintenance costs, and improve safety and efficiency.