Fault diagnosis of EHA with few-shot data augmentation technique

As an emerging object in aerospace actuators, electro-hydrostatic actuator (EHA) has the advantages of heavy load capacity and high reliability. An EHA fault diagnosis method based on a few-shot data augmentation technique is proposed to diagnose and isolate possible faults. The sensitive parameters of typical failure modes are demonstrated based on the mathematical model of EHA. By converting multi-dimensional experimental data into two-dimensional grayscale data and extracting local features, the time series characteristics and correlation between different signals can be highlighted. The Wasserstein deep convolutional generative adversarial network (WDCGAN) is used to enhance the EHA small sample data. The diagnostic model WDCGAN-stacked denoised auto encoder (SDAE) combined with WDCGAN and SDAE is proposed to differentiate between multiple types of EHA failures. Compared with the five commonly used fault classification methods, the proposed method can effectively identify the typical fault modes of EHA, with the highest accuracy of fault classification and strong feature extraction ability.

Automated summary

An electro-hydrostatic actuator (EHA) fault diagnosis method based on few-shot data augmentation is proposed to diagnose and isolate potential faults. The method highlights the time series characteristics and correlation between different signals by converting multi-dimensional experimental data into grayscale data and extracting local features. The diagnostic model, combining Wasserstein deep convolutional generative adversarial network (WDCGAN) with stacked denoised auto encoder (SDAE), effectively identifies typical fault modes of EHA with high accuracy and strong feature extraction ability compared to five commonly used fault classification methods.