Gear Degradation Study Using Statistical Time Features and Shallow Neural Networks

Gears are widely recognized as crucial components for power connections and transmission owing to their substantial loading capacity, transmission efficiency, and consistent power output. Given their complexity, they are prone to faults and require continuous condition monitoring (CM). The study proposes an innovative approach that combines signal analysis with data-driven techniques to monitor gear conditions accurately. By employing a shallow neural network (NN) model, which incorporates statistical time features and principal component analysis (PCA), this strategy achieves precise detection of seven distinct levels of gear wear. The feature generation technique utilizes a sliding and overlapping window to extract 15 statistical time features from the tri-axial vibration signals of the gearbox. The PCA has been applied to the feature set (FS) to retain relevant features, avoid overfitting during model training, and visualize the underlying structure of the training data. A comparative analysis with other leading classification methods, applied to both the original and PCA-reduced feature sets reveals that the shallow NN model, particularly when using the PCA-reduced FS, stands out for its exceptional performance. It achieves a remarkable test accuracy of 99.1% on experimental data having a linear time complexity, highlighting the model’s practicality for real-world applications.