# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
###### Abstract
When failures occur, the fault mode can be quickly identified with fault data, which significantly improves the efficiency of fault diagnosis. Especially for intricate electromechanical systems like the aircraft EHA, collecting the operational fault data and establishing a data-based fault diagnosis model has great practical value in improving the efficiency of fault diagnosis. As a popular tool in data-based fault diagnosis, the convolutional neural network has performed well in recent years and benefits from its sparsity, shared weights, and other advantages. [PERSON] proposed a hybrid fault diagnosis algorithm combining a one-dimensional convolutional neural network and support vector machine, which shows high precision in fault diagnosis of rolling bearings [12]. [PERSON] studied the application of CNN in fault diagnosis of aircraft hydraulic systems, the 1 DMCCNN model proposed by [PERSON] realized the processing of one-dimensional time series signals and multi-sensor fusion [13]. [PERSON] combined the hydraulic cylinder pressure signals into a two-dimensional matrix and fed it into CNN to diagnose the leakage fault of hydraulic cylinders [14]. [PERSON] encoded the one-dimensional bearing vibration signals into the time-series images and then used them as input for two-dimensional CNN for fault diagnosis. The result shows that the encoded images have a stronger ability to express the original fault features, which gives full play to the advantages of two-dimensional CNN in image processing [15]. But, without the proper selection of hyperparameters, such as the number of filters, types of activation function, training batch size, learning rate, etc., CNN cannot perform well in the fault diagnosis. Usually, people adjust the hyperparameters manually based on experience and through constant trial and error, but new CNN tends to have more layers, leading to a surge in the number of hyperparameters. Therefore, it is almost impossible to pinpoint a close-to-optimal hyper-parameter configuration for a CNN manually under a reasonable cost, which hampers the adaption of CNNs for various real-world problems [15]. Hence, several CNN optimization methods for hyper-parameter selection have been proposed in recent years, including the particle swarm optimization, a well-known SI algorithm, which has drawn much interest from researchers for optimizing CNNand other neural networks. The iteration rate and learning effectiveness of CNN can be increased through structural optimization of CNN using the PSO algorithm [16; 17]. The studies indicate that CNN, as a data-based fault diagnosis method, can make up for the shortcomings of traditional fault diagnosis methods. Moreover, the 1 DCNN can process sequence data effectively, while the 2 DCNN has more advantages in image processing. Therefore, we propose a fault diagnosis method based on FCNN for the fault diagnosis of the aircraft EHA. FCNN combines the fault features of two dimensions, enriches the fault feature data of various dimensions, and makes it possible to efficiently extract fault features from numerous system characteristic parameters of aircraft EHA. Moreover, the MSPSO is employed to optimize the FCNN model's hyperparameters. The MSPSO algorithm combines multiple strategies; compared with PSO, a better combination of FCNN hyperparameters can be obtained. The FCNN model and MSPSO algorithm are expounded in Sections 2 and 3. In Section 4, the process of MSPSO optimizing the hyperparameters of the FCNN model is mainly introduced. Section 5 describes how to obtain the fault data of the aircraft EHA through the EHA-FPVS model built-in lab, and how to construct fault samples. In Section 6, the proposed MSPSO-FCNN model is compared with other models, and the compare results are analyzed and discussed. The conclusion is summarized in Section 7.