Identification of engine faults based on acoustic emission signals using a 1DCNN-ViT ensemble model
Shuo Wang, Tong Liu, Kaiyuan Luo, Guoan Yang
Abstract
Abstract In view of the complexity of the engine mechanical structure and the diversity of faults, this paper presents a one-dimensional convolutional neural network (1DCNN)-vision transformer (ViT) ensemble model for identifying engine faults based on acoustic emission (AE) signals. The 1DCNN-ViT ensemble model combines 1DCNN and ViT. Firstly, AE signals of various faults are collected on the engine fault test rig. The dataset is constructed from its High-Mel Filterbank feature, which applies to AE signals. The proposed model has advantageous performance on this dataset. Secondly, the proposed model has a higher test accuracy than other new models. Finally, the fault data with different signal-to-noise ratios are input into the trained models, and the proposed model has better anti-noise performance. Overall, the proposed method can more accurately identify the AE signals of engine faults. It can be used as an effective method to diagnose engine faults.