Fault Identification of Direct-Shift Gearbox Using Variational Mode Decomposition and Convolutional Neural Network

Abstract

The direct-shift gearbox is widely used in many applications, such as automotive and aerospace, due to its large transmission ratio and high transmission efficiency. Rough and heavy-duty working conditions induce various faults, such as scratches, fatigue cracks, pitting, and missing teeth due to breakage. These defects may lead to the failure of one or more components attached to an automatic transmission system. A fault identification scheme for the direct-shift gearbox has been developed, making use of variational mode decomposition (VMD) and convolutional neural network (CNN). The acquired raw signal from the gearbox under different health conditions (healthy, pitting, and chipping) is decomposed into different modes using VMD. The prominent mode is selected based on kurtosis, which is utilized to obtain scalograms. An image matrix is formed utilizing scalograms. Such matrices from different scalograms are divided into training and testing matrices. The training matrices train the CNN model, whereas the testing matrices validate the efficacy of the built CNN model. The proposed scheme identifies faults with 100% accuracy. The proposed scheme has also been compared with other neural networks. These results suggest that the proposed scheme outperforms other networks.

Keywords

• direct-shift gearbox (DSG)
• variational mode decomposition
• convolutional neural network (CNN)
• vibration