MAACCN: An Intelligent Decoupling Diagnosis Method for Compound Faults in Electrohydrostatic Actuators
Yuanhao Hu, Yibo Song, Xiansong He, Xiaoli Zhao, Xiaowei Yang, Jianyong Yao, Zhaoqiang Wang, Hong Pei, Changhua Hu
Abstract
Electro-Hydrostatic Actuators (EHAs), as complex integrated systems of mechanical, electrical, and hydraulic components, play a crucial role in aerospace and other fields. However, due to the complexity of their internal structure and harsh working environments, the compound faults are the most common fault types. Considering the high cost of data collection for uncertainties and complex compound faults in EHA, an intelligent decoupling diagnosis method for compound faults in EHAs based on Maximized Aggregation Attention Convolutional Capsule Network (MAACCN) can be proposed. Firstly, the multi-dimensional sensor information from the EHA can be collected, the feature-level data is fused through a one-dimensional convolutional layer combined with an efficient channel attention mechanism. Secondly, it utilizes the capsule network to extract features deeply and introduces a maximized aggregation routing algorithm between capsule layers. Finally, a decoupling classification layer can be added, its model is optimized by minimizing the margin loss function, enabling the network to more accurately identify and decouple compound faults. Validation on the EHA fault dataset demonstrates that the proposed method achieves higher subset accuracy under different working conditions, which can diagnose compound faults by learning data from single faults.