# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
###### Abstract  
Contrapose the highly integrated, multiple types of faults and complex working conditions of aircraft electro hydrostatic actuator (EHA), to effectively identify its typical faults, we propose a fault diagnosis method based on fusion convolutional neural networks (FCNN). First, the aircraft EHA fault data is encoded by gram angle difference field (GADF) to obtain the fault feature images. Then we build a FCNN model that integrates the 1 DCNN and 2 DCNN, where the original 1D fault data is the input of the 1 DCNN model, and the feature images obtained by GADF transformation are used as the input of 2 DCNN. Multiple convolution and pooling operations are performed on each of these inputs to extract the features. Next these feature vectors are spliced in the convergence layer, and the fully connected layers and the Softmax layers are finally used to attain the classification of aircraft EHA faults. Furthermore, the multi-strategy hybrid particle swarm optimization (MSPSO) algorithm is applied to optimize the FCNN to obtain a better combination of FCNN hyperparameters: MSPSO incorporates various strategies, including an initialization strategy based on homogenization and randomization, and an adaptive inertia weighting strategy, etc. The experimental result indicates that the FCNN model optimized by MSPSO achieves an accuracy of 96.86% for identifying typical faults of the aircraft EHA, respectively, higher than the 1 DCNN and the 2 DCNN by about 16.5% and 5.7%. By comparing with LeNet-5, GoogleNet, AlexNet, and GRU, the FCNN model presents the highest diagnostic accuracy, less time in training and testing. The comprehensive performance of the proposed model is demonstrated to be much stronger. Additionally, the FCNN model improved by MSPSO has a higher accuracy rate when compared to PSO. e 2022 202  
sensor gain), motor circuit failure (short circuit, open circuit, and resistance increases) and hydraulic circuit failure (hydraulic oil pollution, internal leakage), etc. As a future development trend of the aircraft actuation system, how to develop a reliable and effective fault diagnosis method based on the fault signature of EHA has drawn the interest of many academics in recent years. [PERSON], elaborated on the composition and working principle of EHA, described its mathematical model, and provided the theoretical basis for establishing the failure model [4]. With the technology for fault prediction and health management, [PERSON] concentrated on studying the EHA intelligent fault diagnosis and prediction method [5]. [PERSON] proposed an EHA fault detection and isolation analysis method based on the bond graph [6]. [PERSON]. [PERSON] uses genetic algorithms to monitor the performance degradation of EHA [7]. [PERSON] applied the gray correlation analysis to the fault diagnosis of EHA [8]. [PERSON] studied the failure injection of EHA and its effect on aircraft flight performance [9]. Based on the adaptive neural network robust observer, [PERSON] created an EHA fault diagnosis and fault tolerance controller, which successfully improved EHA's robustness to faults [10]. [PERSON] investigates a mathematical model for EHA fault diagnosis and proposes a new model-based fault diagnosis strategy [11]. However, the above-mentioned fault diagnosis methods for EHA are mostly model-based, and there are problems that the linear models established are too large, causing system instability, and the models cannot clearly express the deep logical relationships of the system. As a result, these methods have a poor ability to identify faults, a high rate of false alarms, and poor generalization. Nevertheless, with significant developments in sensors, big data, and artificial intelligence technology, data-based intelligent fault diagnosis methods have been developing rapidly in recent years. They no longer focus on the object's physical structure but analyze the data to find the relationship between fault data and fault mode.