A Novel Framework for Gear Safety Factor Prediction

Gear safety factors are conducive to assessing the reliability of vehicle transmission gears. Due to the insufficiency and high coupling of existing gear data, the prediction of gear safety factors has long been a challenging issue in vehicle transmission industry through learning meaningful representations of high-dimensional gear parameters. This paper presents a framework to find a high-quality solution that improves prediction accuracy of gear safety factors. In the framework, to cope with the insufficiency of gear data, a generative model based on generative adversarial networks is established and proven to generate acceptable data with Adam optimizer. Then, eigen-error principal component analysis is proposed to extract features of gear parameters by reconstructing error function with eigenvectors and eigenvalues. Finally, particle swarm optimization and back propagation are applied to predict safety factors with these extracted features. Experimental results on real-world gear data of vehicle transmissions have validated the effectiveness of our proposed framework.