# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 7 Conclusions  
Combining the advantages of 1 DCNN and 2 DCNN, this paper proposes the FCNN model for typical fault diagnosis of aircraft EHA, which can extract richer fault features from GADF images while retaining the original features in one-dimensional fault data. To further improve the accuracy of fault diagnosis, this paper optimizes the structure of FCNN using the MSPSO algorithm, which combines four effective strategies: the initialization strategy based on homogenization and randomization, the adaptive inertia weights and learning factor strategies, the hybrid variation strategy, and the improved handling method of boundary-crossing particles. Through the process of fault diagnosis, the following conclusion can be reached:  
1. Compared to 1 DCNN, 2 DCNN with GADF map as input performs better in fault diagnosis of aircraft EHA. With the optimization of MSPSO, the 2 DCNN can outperform the 1 DCNN by up to 10.2% when diagnosing typical aircraft EHA faults.
2. FCNN combines the advantages of 1 DCNN and 2 DCNN, and it can extract richer fault features from GADF images while retaining the original features in one-dimensional fault data. With the optimization of MSPSO, the accuracy of FCNN is 96.86%, which is 16.5% and 5.7% higher than 2 DCNN and 1 DCNN, respectively.
3. The original data of aircraft EHA data are highly nonlinear, and encoding them into images by the GADF matrix can weaken the nonlinearity and reduce the noise, which makes 2 DCNN and FCNN have better robustness.
4. The algorithm quickly falls into the local optimum when using the standard PSO to optimize the CNN structure. In comparison, the MSPSO optimization algorithm proposed in the paper benefits from its integration of multiple strategies, can jump from the local optimum, and has better ergodicity. The accuracy of 1 DCNN, 2 DCNN, and FCNN under MSPSO optimization improved by 4.6%, 8.3%, and 8.6%, respectively, over PSO.
5. Through comprehensive comparison with the LeNet-5, GoogleNet, AlexNet, and GRU models, the proposed MSPSO model possesses the highest test accuracy and shorter training time, which is very suitable for aircraft EHA fault diagnosis.  
The proposed fault diagnosis method based on MSPSO-FCNN can effectively diagnose typical faults of aircraft EHA by only relying on the EHA state parameter information collected by sensors, without resorting to traditional complicated technical means or establishing an accurate analytical model of the system, which is superior to some traditional fault diagnosis methods. In the future, we will continue to apply this method to other fault diagnosis fields, such as aircraft engine fault diagnosis, aircraft landing gear faults, etc., and explore a fault diagnosis method with more expansive application fields based on MSPSO-FCNN.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12178562/s1](https://www.mdpi.com/article/10.3390/app12178562/s1)]([https://www.mdpi.com/article/10.3390/app12178562/s1](https://www.mdpi.com/article/10.3390/app12178562/s1)), Table S1: Model parameters of EHA-FPVS.  
Manuscript Writing, X.L.; manuscript review and editing, Y.L. and Y.C.; simulation, X.L. and S.D.; project funding: Y.L.; formal analysis, X.L. and X.W.; reference and data collation, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Aero-Science Fund of China, grant number (20200033052001) and Nanjing University of Aeronautics and Astronautics Postgraduate Innovation Base Open Fund, grant number (kfjj20200725).  
Not applicable.